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# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "464.40 +/- 71.20", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Yukino666/Reinforce-CartPole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-09T04:08:01+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 800_STEPS_1e7_SFT
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 800
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6169 | 0.1 | 50 | 1.6126 |
| 1.5653 | 0.2 | 100 | 1.5784 |
| 1.5233 | 0.29 | 150 | 1.5243 |
| 1.4765 | 0.39 | 200 | 1.4786 |
| 1.4455 | 0.49 | 250 | 1.4396 |
| 1.4057 | 0.59 | 300 | 1.4084 |
| 1.3854 | 0.68 | 350 | 1.3842 |
| 1.3654 | 0.78 | 400 | 1.3669 |
| 1.3416 | 0.88 | 450 | 1.3541 |
| 1.3459 | 0.98 | 500 | 1.3465 |
| 1.3321 | 1.07 | 550 | 1.3419 |
| 1.3314 | 1.17 | 600 | 1.3402 |
| 1.333 | 1.27 | 650 | 1.3395 |
| 1.3389 | 1.37 | 700 | 1.3395 |
| 1.3264 | 1.46 | 750 | 1.3394 |
| 1.3453 | 1.56 | 800 | 1.3394 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "800_STEPS_1e7_SFT", "results": []}]} | text-generation | tsavage68/800_STEPS_1e7_SFT_zeroshot | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T04:15:22+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 800\_STEPS\_1e7\_SFT
====================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3394
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 800
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
80,
145,
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 800### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
# Model Card for Breeze-7B-Cantonese-v0.1
Breeze-7B is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use. Credit to [MediaTek-Research](https://huggingface.co/MediaTek-Research).<br>
Breeze-7B係一個以[Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)作為基礎，為正體中文而造嘅模型系列，由[MediaTek-Research](https://huggingface.co/MediaTek-Research)製作.
[Breeze-7B-Cantonese] derives from the base model [Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1), with finetuning by datasets from [hon9kon9ize](https://huggingface.co/hon9kon9ize/), making the resulting model to be able to chat with Cantonese.<br>
[Breeze-7B-Cantonese] 係由基座模型 [Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1) 衍生出黎，用[hon9kon9ize](https://huggingface.co/hon9kon9ize/) 整嘅數據集微調, 令到呢個模型可以講廣東話。
I selected the [Breeze-7B-Base] model due to its extensive vocabulary coverage tailored for Traditional Chinese. Its language style aligns closely with the nuances of Hong Kong discourse, making it a suitable choice for this project.<br>
我揀[Breeze-7B-Base]做基座模型係因為佢有正體中文嘅擴增詞表, 而且佢嘅語言根基同香港比較相似, 所以佢似乎比較適合呢個項目。
The axolotl config file [axolotl-config.yml](axolotl-config.yml) is shared for open-source purposes, allowing everyone to utilize it for training on their own.<br>
為咗人人都可以自己訓練模型，我放埋個axolotl設定檔 [axolotl-config.yml](axolotl-config.yml)出嚟當開放源始碼。
Thanks for the great datasets from [hon9kon9ize](https://huggingface.co/hon9kon9ize/) and [indiejoseph](https://huggingface.co/indiejoseph), this project owes its existence to their invaluable contributions.<br>
多得[hon9kon9ize](https://huggingface.co/hon9kon9ize/) 同 [indiejoseph](https://huggingface.co/indiejoseph) 放出嚟嘅數據集, 先至有呢個項目出現。 | {"language": ["zh", "en"], "license": "apache-2.0", "tags": ["cantonese", "yue", "hong kong", "\u9999\u6e2f", "\u5ee3\u6771\u8a71", "\u7cb5\u8a9e"], "datasets": ["hon9kon9ize/yue-alpaca", "indiejoseph/wikipedia-translate-zhhk-zhcn", "indiejoseph/wikipedia-zh-yue-summaries", "indiejoseph/wikipedia-zh-yue-qa"], "pipeline_tag": "text-generation"} | text-generation | kennylam/Breeze-7B-Cantonese-v0.1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"cantonese",
"yue",
"hong kong",
"香港",
"廣東話",
"粵語",
"conversational",
"zh",
"en",
"dataset:hon9kon9ize/yue-alpaca",
"dataset:indiejoseph/wikipedia-translate-zhhk-zhcn",
"dataset:indiejoseph/wikipedia-zh-yue-summaries",
"dataset:indiejoseph/wikipedia-zh-yue-qa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T04:17:52+00:00 | [] | [
"zh",
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #cantonese #yue #hong kong #香港 #廣東話 #粵語 #conversational #zh #en #dataset-hon9kon9ize/yue-alpaca #dataset-indiejoseph/wikipedia-translate-zhhk-zhcn #dataset-indiejoseph/wikipedia-zh-yue-summaries #dataset-indiejoseph/wikipedia-zh-yue-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Breeze-7B-Cantonese-v0.1
Breeze-7B is a language model family that builds on top of Mistral-7B, specifically intended for Traditional Chinese use. Credit to MediaTek-Research.<br>
Breeze-7B係一個以Mistral-7B作為基礎，為正體中文而造嘅模型系列，由MediaTek-Research製作.
[Breeze-7B-Cantonese] derives from the base model Breeze-7B-Base, with finetuning by datasets from hon9kon9ize, making the resulting model to be able to chat with Cantonese.<br>
[Breeze-7B-Cantonese] 係由基座模型 Breeze-7B-Base 衍生出黎，用hon9kon9ize 整嘅數據集微調, 令到呢個模型可以講廣東話。
I selected the [Breeze-7B-Base] model due to its extensive vocabulary coverage tailored for Traditional Chinese. Its language style aligns closely with the nuances of Hong Kong discourse, making it a suitable choice for this project.<br>
我揀[Breeze-7B-Base]做基座模型係因為佢有正體中文嘅擴增詞表, 而且佢嘅語言根基同香港比較相似, 所以佢似乎比較適合呢個項目。
The axolotl config file URL is shared for open-source purposes, allowing everyone to utilize it for training on their own.<br>
為咗人人都可以自己訓練模型，我放埋個axolotl設定檔 axolotl-config.yml出嚟當開放源始碼。
Thanks for the great datasets from hon9kon9ize and indiejoseph, this project owes its existence to their invaluable contributions.<br>
多得hon9kon9ize 同 indiejoseph 放出嚟嘅數據集, 先至有呢個項目出現。 | [
"# Model Card for Breeze-7B-Cantonese-v0.1\n\nBreeze-7B is a language model family that builds on top of Mistral-7B, specifically intended for Traditional Chinese use. Credit to MediaTek-Research.<br>\nBreeze-7B係一個以Mistral-7B作為基礎，為正體中文而造嘅模型系列，由MediaTek-Research製作.\n\n[Breeze-7B-Cantonese] derives from the base model Breeze-7B-Base, with finetuning by datasets from hon9kon9ize, making the resulting model to be able to chat with Cantonese.<br>\n[Breeze-7B-Cantonese] 係由基座模型 Breeze-7B-Base 衍生出黎，用hon9kon9ize 整嘅數據集微調, 令到呢個模型可以講廣東話。\n\nI selected the [Breeze-7B-Base] model due to its extensive vocabulary coverage tailored for Traditional Chinese. Its language style aligns closely with the nuances of Hong Kong discourse, making it a suitable choice for this project.<br>\n我揀[Breeze-7B-Base]做基座模型係因為佢有正體中文嘅擴增詞表, 而且佢嘅語言根基同香港比較相似, 所以佢似乎比較適合呢個項目。\n\n\nThe axolotl config file URL is shared for open-source purposes, allowing everyone to utilize it for training on their own.<br>\n為咗人人都可以自己訓練模型，我放埋個axolotl設定檔 axolotl-config.yml出嚟當開放源始碼。\n\nThanks for the great datasets from hon9kon9ize and indiejoseph, this project owes its existence to their invaluable contributions.<br>\n多得hon9kon9ize 同 indiejoseph 放出嚟嘅數據集, 先至有呢個項目出現。"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #cantonese #yue #hong kong #香港 #廣東話 #粵語 #conversational #zh #en #dataset-hon9kon9ize/yue-alpaca #dataset-indiejoseph/wikipedia-translate-zhhk-zhcn #dataset-indiejoseph/wikipedia-zh-yue-summaries #dataset-indiejoseph/wikipedia-zh-yue-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Breeze-7B-Cantonese-v0.1\n\nBreeze-7B is a language model family that builds on top of Mistral-7B, specifically intended for Traditional Chinese use. Credit to MediaTek-Research.<br>\nBreeze-7B係一個以Mistral-7B作為基礎，為正體中文而造嘅模型系列，由MediaTek-Research製作.\n\n[Breeze-7B-Cantonese] derives from the base model Breeze-7B-Base, with finetuning by datasets from hon9kon9ize, making the resulting model to be able to chat with Cantonese.<br>\n[Breeze-7B-Cantonese] 係由基座模型 Breeze-7B-Base 衍生出黎，用hon9kon9ize 整嘅數據集微調, 令到呢個模型可以講廣東話。\n\nI selected the [Breeze-7B-Base] model due to its extensive vocabulary coverage tailored for Traditional Chinese. Its language style aligns closely with the nuances of Hong Kong discourse, making it a suitable choice for this project.<br>\n我揀[Breeze-7B-Base]做基座模型係因為佢有正體中文嘅擴增詞表, 而且佢嘅語言根基同香港比較相似, 所以佢似乎比較適合呢個項目。\n\n\nThe axolotl config file URL is shared for open-source purposes, allowing everyone to utilize it for training on their own.<br>\n為咗人人都可以自己訓練模型，我放埋個axolotl設定檔 axolotl-config.yml出嚟當開放源始碼。\n\nThanks for the great datasets from hon9kon9ize and indiejoseph, this project owes its existence to their invaluable contributions.<br>\n多得hon9kon9ize 同 indiejoseph 放出嚟嘅數據集, 先至有呢個項目出現。"
] | [
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #cantonese #yue #hong kong #香港 #廣東話 #粵語 #conversational #zh #en #dataset-hon9kon9ize/yue-alpaca #dataset-indiejoseph/wikipedia-translate-zhhk-zhcn #dataset-indiejoseph/wikipedia-zh-yue-summaries #dataset-indiejoseph/wikipedia-zh-yue-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# safety-utcustom-train-SF30-RGB-b0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7492
- Mean Iou: 0.3878
- Mean Accuracy: 0.8431
- Overall Accuracy: 0.9233
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.7575
- Accuracy Unsafe: 0.9287
- Iou Unlabeled: 0.0
- Iou Safe: 0.2418
- Iou Unsafe: 0.9214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.1527 | 5.0 | 10 | 1.1085 | 0.0590 | 0.4585 | 0.1664 | nan | 0.7704 | 0.1465 | 0.0 | 0.0307 | 0.1464 |
| 1.1326 | 10.0 | 20 | 1.1091 | 0.0963 | 0.6082 | 0.2699 | nan | 0.9695 | 0.2470 | 0.0 | 0.0419 | 0.2470 |
| 1.0981 | 15.0 | 30 | 1.0980 | 0.1530 | 0.6989 | 0.4242 | nan | 0.9922 | 0.4055 | 0.0 | 0.0535 | 0.4055 |
| 1.086 | 20.0 | 40 | 1.0822 | 0.1916 | 0.7515 | 0.5256 | nan | 0.9927 | 0.5103 | 0.0 | 0.0644 | 0.5103 |
| 1.0466 | 25.0 | 50 | 1.0541 | 0.2226 | 0.7909 | 0.6043 | nan | 0.9902 | 0.5917 | 0.0 | 0.0761 | 0.5917 |
| 1.0533 | 30.0 | 60 | 1.0249 | 0.2444 | 0.8167 | 0.6580 | nan | 0.9863 | 0.6472 | 0.0 | 0.0861 | 0.6471 |
| 0.9779 | 35.0 | 70 | 1.0010 | 0.2607 | 0.8322 | 0.6966 | nan | 0.9771 | 0.6874 | 0.0 | 0.0951 | 0.6871 |
| 0.9161 | 40.0 | 80 | 0.9695 | 0.2808 | 0.8487 | 0.7412 | nan | 0.9635 | 0.7339 | 0.0 | 0.1091 | 0.7334 |
| 0.9843 | 45.0 | 90 | 0.9403 | 0.3004 | 0.8631 | 0.7823 | nan | 0.9494 | 0.7768 | 0.0 | 0.1254 | 0.7759 |
| 0.9568 | 50.0 | 100 | 0.9071 | 0.3176 | 0.8663 | 0.8169 | nan | 0.9191 | 0.8135 | 0.0 | 0.1412 | 0.8117 |
| 0.8443 | 55.0 | 110 | 0.8627 | 0.3403 | 0.8656 | 0.8576 | nan | 0.8742 | 0.8570 | 0.0 | 0.1672 | 0.8537 |
| 0.8765 | 60.0 | 120 | 0.8488 | 0.3450 | 0.8625 | 0.8657 | nan | 0.8591 | 0.8659 | 0.0 | 0.1729 | 0.8620 |
| 0.899 | 65.0 | 130 | 0.8429 | 0.3481 | 0.8629 | 0.8705 | nan | 0.8548 | 0.8710 | 0.0 | 0.1772 | 0.8669 |
| 0.7713 | 70.0 | 140 | 0.8085 | 0.3632 | 0.8497 | 0.8939 | nan | 0.8026 | 0.8969 | 0.0 | 0.1983 | 0.8912 |
| 0.8505 | 75.0 | 150 | 0.7821 | 0.3762 | 0.8465 | 0.9102 | nan | 0.7786 | 0.9145 | 0.0 | 0.2208 | 0.9079 |
| 0.7352 | 80.0 | 160 | 0.7841 | 0.3819 | 0.8392 | 0.9173 | nan | 0.7557 | 0.9226 | 0.0 | 0.2304 | 0.9153 |
| 0.7205 | 85.0 | 170 | 0.7502 | 0.3974 | 0.8400 | 0.9325 | nan | 0.7413 | 0.9388 | 0.0 | 0.2613 | 0.9309 |
| 0.711 | 90.0 | 180 | 0.7417 | 0.3962 | 0.8428 | 0.9313 | nan | 0.7484 | 0.9373 | 0.0 | 0.2591 | 0.9296 |
| 0.7855 | 95.0 | 190 | 0.7281 | 0.4003 | 0.8439 | 0.9343 | nan | 0.7473 | 0.9404 | 0.0 | 0.2683 | 0.9327 |
| 0.7632 | 100.0 | 200 | 0.7494 | 0.3883 | 0.8419 | 0.9237 | nan | 0.7545 | 0.9293 | 0.0 | 0.2430 | 0.9219 |
| 0.8145 | 105.0 | 210 | 0.7495 | 0.3862 | 0.8412 | 0.9219 | nan | 0.7551 | 0.9274 | 0.0 | 0.2387 | 0.9201 |
| 0.8217 | 110.0 | 220 | 0.7355 | 0.3933 | 0.8422 | 0.9282 | nan | 0.7502 | 0.9341 | 0.0 | 0.2533 | 0.9265 |
| 0.7784 | 115.0 | 230 | 0.7258 | 0.4088 | 0.8411 | 0.9413 | nan | 0.7340 | 0.9481 | 0.0 | 0.2864 | 0.9400 |
| 0.8349 | 120.0 | 240 | 0.7492 | 0.3878 | 0.8431 | 0.9233 | nan | 0.7575 | 0.9287 | 0.0 | 0.2418 | 0.9214 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF30-RGB-b0", "results": []}]} | image-segmentation | sam1120/safety-utcustom-train-SF30-RGB-b0 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T04:23:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| safety-utcustom-train-SF30-RGB-b0
=================================
This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7492
* Mean Iou: 0.3878
* Mean Accuracy: 0.8431
* Overall Accuracy: 0.9233
* Accuracy Unlabeled: nan
* Accuracy Safe: 0.7575
* Accuracy Unsafe: 0.9287
* Iou Unlabeled: 0.0
* Iou Safe: 0.2418
* Iou Unsafe: 0.9214
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 9e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 120
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# safety-utcustom-train-SF30-RGB-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Accuracy Safe: 0.8299
- Accuracy Unlabeled: nan
- Accuracy Unsafe: 0.9036
- Iou Safe: 0.3480
- Iou Unlabeled: 0.0
- Iou Unsafe: 0.8996
- Loss: 0.5783
- Mean Accuracy: 0.8668
- Mean Iou: 0.4158
- Overall Accuracy: 0.9013
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 120
### Training results
| Training Loss | Epoch | Step | Accuracy Safe | Accuracy Unlabeled | Accuracy Unsafe | Iou Safe | Iou Unlabeled | Iou Unsafe | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:-----:|:----:|:-------------:|:------------------:|:---------------:|:--------:|:-------------:|:----------:|:---------------:|:-------------:|:--------:|:----------------:|
| 1.0614 | 5.0 | 10 | 0.1904 | nan | 0.5439 | 0.0682 | 0.0 | 0.5350 | 1.0385 | 0.3672 | 0.2011 | 0.5327 |
| 1.0269 | 10.0 | 20 | 0.4801 | nan | 0.5773 | 0.1795 | 0.0 | 0.5719 | 0.9975 | 0.5287 | 0.2505 | 0.5742 |
| 1.0005 | 15.0 | 30 | 0.6270 | nan | 0.6316 | 0.2261 | 0.0 | 0.6269 | 0.9428 | 0.6293 | 0.2843 | 0.6315 |
| 0.9716 | 20.0 | 40 | 0.6870 | nan | 0.6802 | 0.2529 | 0.0 | 0.6756 | 0.8918 | 0.6836 | 0.3095 | 0.6804 |
| 0.9255 | 25.0 | 50 | 0.7339 | nan | 0.7081 | 0.2805 | 0.0 | 0.7037 | 0.8542 | 0.7210 | 0.3281 | 0.7089 |
| 0.9256 | 30.0 | 60 | 0.7705 | nan | 0.7229 | 0.2781 | 0.0 | 0.7189 | 0.8330 | 0.7467 | 0.3324 | 0.7244 |
| 0.8167 | 35.0 | 70 | 0.7622 | nan | 0.7349 | 0.3004 | 0.0 | 0.7311 | 0.8114 | 0.7485 | 0.3438 | 0.7358 |
| 0.7927 | 40.0 | 80 | 0.7776 | nan | 0.7594 | 0.3154 | 0.0 | 0.7559 | 0.7793 | 0.7685 | 0.3571 | 0.7600 |
| 0.8227 | 45.0 | 90 | 0.8020 | nan | 0.7821 | 0.3152 | 0.0 | 0.7789 | 0.7574 | 0.7920 | 0.3647 | 0.7827 |
| 0.81 | 50.0 | 100 | 0.8114 | nan | 0.7983 | 0.3140 | 0.0 | 0.7955 | 0.7370 | 0.8049 | 0.3698 | 0.7987 |
| 0.7198 | 55.0 | 110 | 0.8002 | nan | 0.8194 | 0.3303 | 0.0 | 0.8162 | 0.7118 | 0.8098 | 0.3822 | 0.8188 |
| 0.7523 | 60.0 | 120 | 0.7877 | nan | 0.8482 | 0.3457 | 0.0 | 0.8443 | 0.6832 | 0.8179 | 0.3967 | 0.8462 |
| 0.7239 | 65.0 | 130 | 0.8112 | nan | 0.8485 | 0.3197 | 0.0 | 0.8453 | 0.6745 | 0.8298 | 0.3883 | 0.8473 |
| 0.6235 | 70.0 | 140 | 0.7906 | nan | 0.8686 | 0.3507 | 0.0 | 0.8649 | 0.6419 | 0.8296 | 0.4052 | 0.8662 |
| 0.6887 | 75.0 | 150 | 0.7951 | nan | 0.8758 | 0.3568 | 0.0 | 0.8720 | 0.6302 | 0.8354 | 0.4096 | 0.8732 |
| 0.6079 | 80.0 | 160 | 0.8069 | nan | 0.8879 | 0.3561 | 0.0 | 0.8841 | 0.6120 | 0.8474 | 0.4134 | 0.8853 |
| 0.6022 | 85.0 | 170 | 0.8126 | nan | 0.9062 | 0.3699 | 0.0 | 0.9020 | 0.5849 | 0.8594 | 0.4240 | 0.9032 |
| 0.5748 | 90.0 | 180 | 0.8053 | nan | 0.9047 | 0.3793 | 0.0 | 0.9005 | 0.5802 | 0.8550 | 0.4266 | 0.9016 |
| 0.6228 | 95.0 | 190 | 0.8164 | nan | 0.9050 | 0.3624 | 0.0 | 0.9007 | 0.5793 | 0.8607 | 0.4210 | 0.9022 |
| 0.5332 | 100.0 | 200 | 0.8214 | nan | 0.9134 | 0.3623 | 0.0 | 0.9091 | 0.5616 | 0.8674 | 0.4238 | 0.9105 |
| 0.6655 | 105.0 | 210 | 0.8262 | nan | 0.9072 | 0.3572 | 0.0 | 0.9031 | 0.5688 | 0.8667 | 0.4201 | 0.9046 |
| 0.5835 | 110.0 | 220 | 0.8233 | nan | 0.9092 | 0.3599 | 0.0 | 0.9050 | 0.5653 | 0.8662 | 0.4216 | 0.9064 |
| 0.5764 | 115.0 | 230 | 0.8099 | nan | 0.9165 | 0.3783 | 0.0 | 0.9120 | 0.5460 | 0.8632 | 0.4301 | 0.9131 |
| 0.5621 | 120.0 | 240 | 0.8299 | nan | 0.9036 | 0.3480 | 0.0 | 0.8996 | 0.5783 | 0.8668 | 0.4158 | 0.9013 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF30-RGB-b5", "results": []}]} | image-segmentation | sam1120/safety-utcustom-train-SF30-RGB-b5 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T04:24:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| safety-utcustom-train-SF30-RGB-b5
=================================
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
* Accuracy Safe: 0.8299
* Accuracy Unlabeled: nan
* Accuracy Unsafe: 0.9036
* Iou Safe: 0.3480
* Iou Unlabeled: 0.0
* Iou Unsafe: 0.8996
* Loss: 0.5783
* Mean Accuracy: 0.8668
* Mean Iou: 0.4158
* Overall Accuracy: 0.9013
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 120
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "-3.80 +/- 2.40", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Yukino666/Pixelcopter-PLE-v0 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-09T04:29:25+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# billsum_4875_t5-v1_1-base
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5157
- Rouge1: 0.1576
- Rouge2: 0.0565
- Rougel: 0.128
- Rougelsum: 0.1279
- Gen Len: 18.9871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.9403 | 6.75 | 500 | 2.5157 | 0.1576 | 0.0565 | 0.128 | 0.1279 | 18.9871 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/t5-v1_1-base", "model-index": [{"name": "billsum_4875_t5-v1_1-base", "results": []}]} | text2text-generation | ryusangwon/billsum_4875_t5-v1_1-base | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/t5-v1_1-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T04:30:11+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| billsum\_4875\_t5-v1\_1-base
============================
This model is a fine-tuned version of google/t5-v1\_1-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5157
* Rouge1: 0.1576
* Rouge2: 0.0565
* Rougel: 0.128
* Rougelsum: 0.1279
* Gen Len: 18.9871
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.0.1+cu117
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
78,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m-squad-model3
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 95
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-squad-model3", "results": []}]} | question-answering | varun-v-rao/opt-350m-squad-model3 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:facebook/opt-350m",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T04:34:00+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-squad-model3
This model is a fine-tuned version of facebook/opt-350m on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 95
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# opt-350m-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 95\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 95\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
81,
35,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-350m-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 95\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | peft |
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "bigscience/bloomz-560m"} | null | HackerCIS/bloomz-560m_PROMPT_TUNING_CAUSAL_SPAM | [
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|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft_zephyr
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceH4/zephyr-7b-alpha", "model-index": [{"name": "sft_zephyr", "results": []}]} | null | 4ndr3w/sft_zephyr | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"license:mit",
"region:us"
] | 2024-02-09T04:44:08+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us
|
# sft_zephyr
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us \n",
"# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-HuggingFaceH4/zephyr-7b-alpha #license-mit #region-us \n# sft_zephyr\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-alpha on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 5### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter_v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "56.50 +/- 29.93", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | vadhri/Reinforce-pixelcopter_v1 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-09T04:50:19+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mlm_soft_finetune_bert
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0020
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.0058 | 0 |
| 0.0032 | 1 |
| 0.0029 | 2 |
| 0.0025 | 3 |
| 0.0024 | 4 |
| 0.0023 | 5 |
| 0.0022 | 6 |
| 0.0021 | 7 |
| 0.0021 | 8 |
| 0.0020 | 9 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilroberta-base", "model-index": [{"name": "mlm_soft_finetune_bert", "results": []}]} | fill-mask | ubaskota/mlm_soft_finetune_bert | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T04:51:47+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mlm\_soft\_finetune\_bert
=========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0020
* Epoch: 9
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 1e-04, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.37.2
* TensorFlow 2.15.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-04, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-04, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Tokenizers 0.15.1"
] | [
66,
196,
4,
25
] | [
"passage: TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 1e-04, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* TensorFlow 2.15.0\n* Tokenizers 0.15.1"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2-query_test100data
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-2-query_test100data", "results": []}]} | null | akashAD/phi-2-query_test100data | [
"peft",
"tensorboard",
"safetensors",
"phi",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"license:mit",
"region:us"
] | 2024-02-09T05:03:23+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us
|
# phi-2-query_test100data
This model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# phi-2-query_test100data\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n",
"# phi-2-query_test100data\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n# phi-2-query_test100data\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6"} | null | aneet/tinyllava_next_lora | [
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"arxiv:1910.09700",
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#peft #safetensors #llava_llama #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v0.6 #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
# 🌔 moondream1
1.6B parameter model built by [@vikhyatk](https://x.com/vikhyatk) using SigLIP, Phi-1.5 and the LLaVa training dataset.
The model is release for research purposes only, commercial use is not allowed.
Try it out on [Huggingface Spaces](https://huggingface.co/spaces/vikhyatk/moondream1)!
**Usage**
```
pip install transformers timm einops
```
```python
from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer
from PIL import Image
model_id = "vikhyatk/moondream1"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained(model_id)
image = Image.open('<IMAGE_PATH>')
enc_image = model.encode_image(image)
print(model.answer_question(enc_image, "<QUESTION>", tokenizer))
```
## Benchmarks
| Model | Parameters | VQAv2 | GQA | TextVQA |
| --- | --- | --- | --- | --- |
| LLaVA-1.5 | 13.3B | 80.0 | 63.3 | 61.3 |
| LLaVA-1.5 | 7.3B | 78.5 | 62.0 | 58.2 |
| **moondream1** | 1.6B | 74.7 | 57.9 | 35.6 |
## Examples
| Image | Examples |
| --- | --- |
|  | **What is the title of this book?**<br>The Little Book of Deep Learning<br><br>**What can you tell me about this book?**<br>The book in the image is titled "The Little Book of Deep Learning." It appears to be a guide or manual that provides information and instructions on the subject of deep learning. The book is described as being small and thin, making it easy to carry and read. It is set in a room with a view outside, suggesting that the person holding the book might be in a living or working space with a view. The content of the book is likely to be complex, as the title implies a focus on advanced concepts in deep learning.<br><br>**Where is the person standing?**<br> The person is standing on a balcony or deck, which is outside. |
|  | **What type of food is the girl holding?**<br>The girl is holding a hamburger.<br><br>**What color is the woman's hair?**<br>The woman's hair is white.<br><br>**What is the girl doing in the image?**<br>The girl in the image is eating a hamburger. |
|  | **What color is the train?**<br>The train is red.<br><br>**What is the bus doing in the image?**<br> The bus in the image is driving down a city street, passing through an intersection.<br><br>**What kind of public transportation is in the image?**<br>The image features a red train or trolley as the public transportation option. |
|  | **What is the girl looking at?**<br>The girl is looking at her reflection in the mirror.<br><br>**What color is the woman's hair?**<br>The woman's hair is brown.<br><br>**What is the woman doing in the mirror?**<br>The woman is adjusting her hair in the mirror. |
|  | **What might be the reason for the dog's aggressive behavior?**<br>The dog's aggressive behavior, including its growling, baring its teeth, and showing its sharp teeth, could be due to various reasons. It is possible that the dog is feeling threatened, scared, or defensive about something in its environment, such as another animal, a loud noise, or a person nearby. Alternatively, the dog might be reacting to a command it received from its owner, or it could be experiencing discomfort or pain, which is causing it to display aggressive behavior. It is essential to observe the dog's body language and context to determine the underlying cause of its aggressive behavior and respond accordingly to address its needs or concerns.<br><br>**What kind of dog is in the picture?**<br>The dog in the picture is a small, black and white chihuahua. |
| {"language": ["en"]} | text-generation | sujitvasanth/vikhyatk-moondream1.1old | [
"transformers",
"safetensors",
"moondream1",
"text-generation",
"custom_code",
"en",
"autotrain_compatible",
"region:us"
] | 2024-02-09T05:10:13+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #moondream1 #text-generation #custom_code #en #autotrain_compatible #region-us
| moondream1
==========
1.6B parameter model built by @vikhyatk using SigLIP, Phi-1.5 and the LLaVa training dataset.
The model is release for research purposes only, commercial use is not allowed.
Try it out on Huggingface Spaces!
Usage
Benchmarks
----------
Examples
--------
| [] | [
"TAGS\n#transformers #safetensors #moondream1 #text-generation #custom_code #en #autotrain_compatible #region-us \n"
] | [
39
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"passage: TAGS\n#transformers #safetensors #moondream1 #text-generation #custom_code #en #autotrain_compatible #region-us \n"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-bn-adapter-6.34M-squad-model1
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 58
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-large", "model-index": [{"name": "t5-large-bn-adapter-6.34M-squad-model1", "results": []}]} | null | varun-v-rao/t5-large-bn-adapter-6.34M-squad-model1 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:t5-large",
"license:apache-2.0",
"region:us"
] | 2024-02-09T05:13:19+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #region-us
|
# t5-large-bn-adapter-6.34M-squad-model1
This model is a fine-tuned version of t5-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 58
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# t5-large-bn-adapter-6.34M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 58\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #region-us \n",
"# t5-large-bn-adapter-6.34M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 58\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #region-us \n# t5-large-bn-adapter-6.34M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 58\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-lora-1.77M-squad-model3
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 74
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-base", "model-index": [{"name": "t5-base-lora-1.77M-squad-model3", "results": []}]} | question-answering | varun-v-rao/t5-base-lora-1.77M-squad-model3 | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:t5-base",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T05:15:51+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-lora-1.77M-squad-model3
This model is a fine-tuned version of t5-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 74
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# t5-base-lora-1.77M-squad-model3\n\nThis model is a fine-tuned version of t5-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-lora-1.77M-squad-model3\n\nThis model is a fine-tuned version of t5-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
80,
37,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n# t5-base-lora-1.77M-squad-model3\n\nThis model is a fine-tuned version of t5-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 74\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_1024-3e-4
This model was trained from scratch on the kanishka/counterfactual-babylm-random_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3909
- Accuracy: 0.4102
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7399 | 1.0 | 18586 | 3.9066 | 0.3474 |
| 3.4368 | 2.0 | 37172 | 3.6279 | 0.3750 |
| 3.294 | 3.0 | 55758 | 3.4854 | 0.3884 |
| 3.2094 | 4.0 | 74344 | 3.4178 | 0.3968 |
| 3.1515 | 5.0 | 92930 | 3.3861 | 0.4009 |
| 3.1023 | 6.0 | 111516 | 3.3600 | 0.4041 |
| 3.0643 | 7.0 | 130102 | 3.3565 | 0.4047 |
| 3.0294 | 8.0 | 148688 | 3.3575 | 0.4059 |
| 3.0007 | 9.0 | 167274 | 3.3660 | 0.4068 |
| 2.9771 | 10.0 | 185860 | 3.3513 | 0.4075 |
| 2.9526 | 11.0 | 204446 | 3.3433 | 0.4092 |
| 2.9307 | 12.0 | 223032 | 3.3542 | 0.4094 |
| 2.91 | 13.0 | 241618 | 3.3446 | 0.4095 |
| 2.888 | 14.0 | 260204 | 3.3463 | 0.4100 |
| 2.862 | 15.0 | 278790 | 3.3530 | 0.4103 |
| 2.8465 | 16.0 | 297376 | 3.3666 | 0.4098 |
| 2.8291 | 17.0 | 315962 | 3.3780 | 0.4099 |
| 2.8072 | 18.0 | 334548 | 3.3858 | 0.4099 |
| 2.786 | 19.0 | 353134 | 3.3847 | 0.4104 |
| 2.773 | 20.0 | 371720 | 3.3909 | 0.4102 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-random_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_1024-3e-4", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-random_removal", "type": "kanishka/counterfactual-babylm-random_removal"}, "metrics": [{"type": "accuracy", "value": 0.4102484709891008, "name": "Accuracy"}]}]}]} | text-generation | kanishka/smolm-autoreg-bpe-counterfactual-babylm-random_removal-seed_1024-3e-4 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-random_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T05:18:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| smolm-autoreg-bpe-counterfactual-babylm-random\_removal-seed\_1024-3e-4
=======================================================================
This model was trained from scratch on the kanishka/counterfactual-babylm-random\_removal dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3909
* Accuracy: 0.4102
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 1024
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 32000
* num\_epochs: 20.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
82,
132,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-random_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-bn-adapter-3.17M-squad-model3
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 46
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-bn-adapter-3.17M-squad-model3", "results": []}]} | null | varun-v-rao/roberta-large-bn-adapter-3.17M-squad-model3 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-large",
"license:mit",
"region:us"
] | 2024-02-09T05:20:31+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #region-us
|
# roberta-large-bn-adapter-3.17M-squad-model3
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 46
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-large-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 46\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #region-us \n",
"# roberta-large-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 46\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
45,
42,
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90,
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33
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"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-large #license-mit #region-us \n# roberta-large-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 46\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 500_STEPS_1e7_SFT
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6169 | 0.1 | 50 | 1.6126 |
| 1.5653 | 0.2 | 100 | 1.5784 |
| 1.524 | 0.29 | 150 | 1.5257 |
| 1.4813 | 0.39 | 200 | 1.4845 |
| 1.4608 | 0.49 | 250 | 1.4560 |
| 1.4351 | 0.59 | 300 | 1.4397 |
| 1.4317 | 0.68 | 350 | 1.4319 |
| 1.4269 | 0.78 | 400 | 1.4300 |
| 1.4167 | 0.88 | 450 | 1.4297 |
| 1.4284 | 0.98 | 500 | 1.4297 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "500_STEPS_1e7_SFT", "results": []}]} | text-generation | tsavage68/500_STEPS_1e7_SFT_zeroshot | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T05:25:20+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 500\_STEPS\_1e7\_SFT
====================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4297
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 500
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
80,
145,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Gpt4_t1_Llama13b_Seed103 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | 2024-02-09T05:34:33+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned_Gpt4_t1_Llama13b_Seed103 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | null | transformers |
# SOLAR-10.7B
### Model Details
- Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2)
### Datasets
- sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- sampling and translate [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- translate [GAIR/lima](https://huggingface.co/datasets/GAIR/lima)
- [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
### Benchmark | {"language": ["ko"], "license": "cc-by-nc-4.0", "tags": ["SOLAR-10.7B"], "pipeline_tag": "text-generation"} | text-generation | hyeogi/SOLAR-10.7B-v1.3 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"SOLAR-10.7B",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T05:36:06+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #SOLAR-10.7B #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SOLAR-10.7B
### Model Details
- Base Model: yanolja/KoSOLAR-10.7B-v0.2
### Datasets
- sampling and translate Open-Orca/SlimOrca
- sampling and translate Anthropic/hh-rlhf
- translate GAIR/lima
- jojo0217/korean_rlhf_dataset
### Benchmark | [
"# SOLAR-10.7B",
"### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2",
"### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset",
"### Benchmark"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #SOLAR-10.7B #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SOLAR-10.7B",
"### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2",
"### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset",
"### Benchmark"
] | [
67,
7,
21,
59,
5
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #SOLAR-10.7B #ko #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SOLAR-10.7B### Model Details\n- Base Model: yanolja/KoSOLAR-10.7B-v0.2### Datasets\n- sampling and translate Open-Orca/SlimOrca\n- sampling and translate Anthropic/hh-rlhf\n- translate GAIR/lima\n- jojo0217/korean_rlhf_dataset### Benchmark"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-bn-adapter-3.17M-squad-model2
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 79
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "bert-large-cased", "model-index": [{"name": "bert-large-cased-bn-adapter-3.17M-squad-model2", "results": []}]} | null | varun-v-rao/bert-large-cased-bn-adapter-3.17M-squad-model2 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:bert-large-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-09T05:41:28+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us
|
# bert-large-cased-bn-adapter-3.17M-squad-model2
This model is a fine-tuned version of bert-large-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 79
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# bert-large-cased-bn-adapter-3.17M-squad-model2\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us \n",
"# bert-large-cased-bn-adapter-3.17M-squad-model2\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
50,
48,
6,
12,
8,
3,
90,
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33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us \n# bert-large-cased-bn-adapter-3.17M-squad-model2\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 79\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | null |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | null | LoneStriker/Trinity-33B-v1.0-GGUF | [
"gguf",
"license:other",
"region:us"
] | 2024-02-09T05:43:55+00:00 | [] | [] | TAGS
#gguf #license-other #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#gguf #license-other #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
14,
17,
40,
25,
23
] | [
"passage: TAGS\n#gguf #license-other #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | diffusers |
# LoRA DreamBooth - mikerjacobi/projectile-lora-model
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks projectile using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "instance_prompt": "a photo of sks projectile", "inference": true} | text-to-image | mikerjacobi/projectile-lora-model | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-09T05:48:21+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
# LoRA DreamBooth - mikerjacobi/projectile-lora-model
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks projectile using DreamBooth. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
LoRA for the text encoder was enabled: False.
| [
"# LoRA DreamBooth - mikerjacobi/projectile-lora-model\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks projectile using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
"TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n",
"# LoRA DreamBooth - mikerjacobi/projectile-lora-model\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks projectile using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
68,
105
] | [
"passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - mikerjacobi/projectile-lora-model\n\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks projectile using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False."
] | [
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null | null | null | Original Model: https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser
```imatrix -m NeuralHermes-2.5-Mistral-7B-laser-8_0.gguf -f group_10_merged.txt -ngl 99 -c 256``` | {"tags": ["laser", "mistral"]} | null | Bakanayatsu/NeuralHermes-2.5-Mistral-7B-laser-imatrix-GGUF | [
"gguf",
"laser",
"mistral",
"region:us"
] | 2024-02-09T05:49:43+00:00 | [] | [] | TAGS
#gguf #laser #mistral #region-us
| Original Model: URL
| [] | [
"TAGS\n#gguf #laser #mistral #region-us \n"
] | [
15
] | [
"passage: TAGS\n#gguf #laser #mistral #region-us \n"
] | [
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] |
null | null | nemo |
# OpenMath-CodeLlama-7b-Python
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td>
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td>
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td>
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td>
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td>
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td>
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- [Code](https://github.com/Kipok/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
See our [paper](https://arxiv.org/abs/2402.10176) for more details!
# How to use the models?
Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
# Reproducing our results
We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
- [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
- [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
- [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
# Citation
If you find our work useful, please consider citing us!
```bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
```
# License
The use of this model is governed by the [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/) | {"language": ["en"], "license": "llama2", "library_name": "nemo", "tags": ["nvidia", "code", "math"], "datasets": ["nvidia/OpenMathInstruct-1"], "base_model": ["codellama/CodeLlama-7b-Python-hf"]} | null | nvidia/OpenMath-CodeLlama-7b-Python | [
"nemo",
"nvidia",
"code",
"math",
"en",
"dataset:nvidia/OpenMathInstruct-1",
"arxiv:2402.10176",
"base_model:codellama/CodeLlama-7b-Python-hf",
"license:llama2",
"region:us"
] | 2024-02-09T05:52:53+00:00 | [
"2402.10176"
] | [
"en"
] | TAGS
#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #region-us
|
# OpenMath-CodeLlama-7b-Python
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on OpenMathInstruct-1,
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
Mixtral-8x7B model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="URL | <a href="URL
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="URL | <a href="URL
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="URL | <a href="URL
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="URL | <a href="URL
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="URL | <a href="URL
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="URL | <a href="URL
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- Code
- Models
- Dataset
See our paper for more details!
# How to use the models?
Try to run inference with our models with just a few commands!
# Reproducing our results
We provide all instructions to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- NeMo-Skills Pipeline
- Generating synthetic data
- Finetuning models
- Evaluating models
In our pipeline we use NVIDIA NeMo,
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
If you find our work useful, please consider citing us!
# License
The use of this model is governed by the Llama 2 Community License Agreement | [
"# OpenMath-CodeLlama-7b-Python\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!",
"# License\n\nThe use of this model is governed by the Llama 2 Community License Agreement"
] | [
"TAGS\n#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #region-us \n",
"# OpenMath-CodeLlama-7b-Python\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!",
"# License\n\nThe use of this model is governed by the Llama 2 Community License Agreement"
] | [
68,
1002,
22,
16,
149,
18
] | [
"passage: TAGS\n#nemo #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #region-us \n"
] | [
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] |
null | null | transformers |
# OpenMath-CodeLlama-7b-Python-hf
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1),
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td>
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td>
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td>
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td>
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td>
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td>
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- [Code](https://github.com/Kipok/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
See our [paper](https://arxiv.org/abs/2402.10176) for more details!
# How to use the models?
Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
# Reproducing our results
We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
- [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
- [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
- [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
# Citation
If you find our work useful, please consider citing us!
```bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
```
# License
The use of this model is governed by the [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/) | {"language": ["en"], "license": "llama2", "tags": ["nvidia", "code", "math"], "datasets": ["nvidia/OpenMathInstruct-1"], "base_model": ["codellama/CodeLlama-7b-Python-hf"]} | text-generation | nvidia/OpenMath-CodeLlama-7b-Python-hf | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"code",
"math",
"en",
"dataset:nvidia/OpenMathInstruct-1",
"arxiv:2402.10176",
"base_model:codellama/CodeLlama-7b-Python-hf",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T05:53:23+00:00 | [
"2402.10176"
] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# OpenMath-CodeLlama-7b-Python-hf
OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks
executed by Python interpreter. The models were trained on OpenMathInstruct-1,
a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed
Mixtral-8x7B model.
<table border="1">
<tr>
<td></td>
<td colspan="2" style="text-align: center;">greedy</td>
<td colspan="2" style="text-align: center;">majority@50</td>
</tr>
<tr>
<td style="text-align: center;">model</td>
<td style="text-align: center;">GSM8K</td>
<td style="text-align: center;">MATH</td>
<td style="text-align: center;">GMS8K</td>
<td style="text-align: center;">MATH</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="URL | <a href="URL
<td style="text-align: center;">75.9</td>
<td style="text-align: center;">43.6</td>
<td style="text-align: center;">84.8</td>
<td style="text-align: center;">55.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Mistral-7B (<a href="URL | <a href="URL
<td style="text-align: center;">80.2</td>
<td style="text-align: center;">44.5</td>
<td style="text-align: center;">86.9</td>
<td style="text-align: center;">57.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="URL | <a href="URL
<td style="text-align: center;">78.8</td>
<td style="text-align: center;">45.5</td>
<td style="text-align: center;">86.8</td>
<td style="text-align: center;">57.6</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="URL | <a href="URL
<td style="text-align: center;">80.7</td>
<td style="text-align: center;">48.3</td>
<td style="text-align: center;">88.0</td>
<td style="text-align: center;">60.2</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-Llama2-70B (<a href="URL | <a href="URL
<td style="text-align: center;"><b>84.7</b></td>
<td style="text-align: center;">46.3</td>
<td style="text-align: center;">90.1</td>
<td style="text-align: center;">58.3</td>
</tr>
<tr>
<td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="URL | <a href="URL
<td style="text-align: center;">84.6</td>
<td style="text-align: center;"><b>50.7</b></td>
<td style="text-align: center;"><b>90.8</b></td>
<td style="text-align: center;"><b>60.4</b></td>
</tr>
</table>
The pipeline we used to produce these models is fully open-sourced!
- Code
- Models
- Dataset
See our paper for more details!
# How to use the models?
Try to run inference with our models with just a few commands!
# Reproducing our results
We provide all instructions to fully reproduce our results.
# Improving other models
To improve other models or to learn more about our code, read through the docs below.
- NeMo-Skills Pipeline
- Generating synthetic data
- Finetuning models
- Evaluating models
In our pipeline we use NVIDIA NeMo,
an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
If you find our work useful, please consider citing us!
# License
The use of this model is governed by the Llama 2 Community License Agreement | [
"# OpenMath-CodeLlama-7b-Python-hf\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!",
"# License\n\nThe use of this model is governed by the Llama 2 Community License Agreement"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# OpenMath-CodeLlama-7b-Python-hf\n\nOpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks\nexecuted by Python interpreter. The models were trained on OpenMathInstruct-1,\na math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed\nMixtral-8x7B model.\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce these models is fully open-sourced!\n\n- Code\n- Models\n- Dataset\n\nSee our paper for more details!",
"# How to use the models?\n\nTry to run inference with our models with just a few commands!",
"# Reproducing our results\n\nWe provide all instructions to fully reproduce our results.",
"# Improving other models\n\nTo improve other models or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIn our pipeline we use NVIDIA NeMo,\nan end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.\nIt includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,\noffering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\nIf you find our work useful, please consider citing us!",
"# License\n\nThe use of this model is governed by the Llama 2 Community License Agreement"
] | [
106,
1005,
22,
16,
149,
18
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #nvidia #code #math #en #dataset-nvidia/OpenMathInstruct-1 #arxiv-2402.10176 #base_model-codellama/CodeLlama-7b-Python-hf #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | golemsystems/Llama-2-7b-chat-boost_try2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-09T05:59:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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[optional]
BibTeX:
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | golemsystems/Llama-2-7b-chat-boost_try | [
"transformers",
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"1910.09700"
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#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
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## Technical Specifications [optional]
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] |
null | null | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: SungjunEom/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | reinforcement-learning | SungjunEom/ppo-SnowballTarget | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2024-02-09T06:03:25+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: SungjunEom/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
50,
208
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | gmonsoon/Qwenchana-0.5B-uncensored | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | setfit |
# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect)
- **SetFitABSA Polarity Model:** [tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul> |
| no aspect | <ul><li>"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
"tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 17.9296 | 37 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 71 |
| aspect | 128 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.3401 | - |
| 0.0370 | 50 | 0.2593 | - |
| 0.0740 | 100 | 0.1743 | - |
| 0.1109 | 150 | 0.0725 | - |
| 0.1479 | 200 | 0.0023 | - |
| 0.1849 | 250 | 0.0005 | - |
| 0.2219 | 300 | 0.0006 | - |
| 0.2589 | 350 | 0.0009 | - |
| 0.2959 | 400 | 0.0002 | - |
| 0.3328 | 450 | 0.0002 | - |
| 0.3698 | 500 | 0.0001 | - |
| 0.4068 | 550 | 0.0001 | - |
| 0.4438 | 600 | 0.0001 | - |
| 0.4808 | 650 | 0.0001 | - |
| 0.5178 | 700 | 0.0001 | - |
| 0.5547 | 750 | 0.0001 | - |
| 0.5917 | 800 | 0.0001 | - |
| 0.6287 | 850 | 0.0001 | - |
| 0.6657 | 900 | 0.0001 | - |
| 0.7027 | 950 | 0.0001 | - |
| 0.7396 | 1000 | 0.0001 | - |
| 0.7766 | 1050 | 0.0001 | - |
| 0.8136 | 1100 | 0.0001 | - |
| 0.8506 | 1150 | 0.0001 | - |
| 0.8876 | 1200 | 0.0001 | - |
| 0.9246 | 1250 | 0.0001 | - |
| 0.9615 | 1300 | 0.0 | - |
| 0.9985 | 1350 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- spaCy: 3.7.3
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
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--> | {"library_name": "setfit", "tags": ["setfit", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "bar:After really enjoying ourselves at the bar we sat down at a table and had dinner."}, {"text": "interior decor:this little place has a cute interior decor and affordable city prices."}, {"text": "cuisine:The cuisine from what I've gathered is authentic Taiwanese, though its very different from what I've been accustomed to in Taipei."}, {"text": "dining:Go here for a romantic dinner but not for an all out wow dining experience."}, {"text": "Taipei:The cuisine from what I've gathered is authentic Taiwanese, though its very different from what I've been accustomed to in Taipei."}], "pipeline_tag": "text-classification", "inference": false, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2"} | text-classification | tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect | [
"setfit",
"safetensors",
"mpnet",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"region:us"
] | 2024-02-09T06:07:22+00:00 | [
"2209.11055"
] | [] | TAGS
#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us
| SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
=======================================================================
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. Use this SetFit model to filter these possible aspect span candidates.
3. Use a SetFit model to classify the filtered aspect span candidates.
Model Details
-------------
### Model Description
* Model Type: SetFit
* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
* Classification head: a LogisticRegression instance
* spaCy Model: en\_core\_web\_lg
* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect
* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity
* Maximum Sequence Length: 512 tokens
* Number of Classes: 2 classes
### Model Sources
* Repository: SetFit on GitHub
* Paper: Efficient Few-Shot Learning Without Prompts
* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
### Model Labels
Uses
----
### Direct Use for Inference
First install the SetFit library:
Then you can load this model and run inference.
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* batch\_size: (16, 2)
* num\_epochs: (1, 16)
* max\_steps: -1
* sampling\_strategy: oversampling
* body\_learning\_rate: (2e-05, 1e-05)
* head\_learning\_rate: 0.01
* loss: CosineSimilarityLoss
* distance\_metric: cosine\_distance
* margin: 0.25
* end\_to\_end: False
* use\_amp: False
* warmup\_proportion: 0.1
* seed: 42
* eval\_max\_steps: -1
* load\_best\_model\_at\_end: False
### Training Results
### Framework Versions
* Python: 3.10.12
* SetFit: 1.0.3
* Sentence Transformers: 2.3.1
* spaCy: 3.7.3
* Transformers: 4.35.2
* PyTorch: 2.1.0+cu121
* Datasets: 2.16.1
* Tokenizers: 0.15.1
### BibTeX
| [
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* spaCy: 3.7.3\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
"TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us \n",
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* spaCy: 3.7.3\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
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"passage: TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False### Training Results"
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null | null | setfit |
# SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect](https://huggingface.co/tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect)
- **SetFitABSA Polarity Model:** [tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity](https://huggingface.co/tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'But the staff was so horrible:But the staff was so horrible to us.'</li><li>', forgot our toast, left out:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li><li>'did not have mayonnaise, forgot our:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li></ul> |
| positive | <ul><li>"factor was the food, which was:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"The food is uniformly exceptional:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>"a very capable kitchen which will proudly:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul> |
| neutral | <ul><li>"'s on the menu or not.:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>'to sample both meats).:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li><li>'to split the dish in half so:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li></ul> |
| conflict | <ul><li>'The food was delicious but:The food was delicious but do not come here on a empty stomach.'</li><li>"The service varys from day:The service varys from day to day- sometimes they're very nice, and sometimes not."</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect",
"tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 6 | 21.3594 | 43 |
| Label | Training Sample Count |
|:---------|:----------------------|
| conflict | 2 |
| negative | 19 |
| neutral | 25 |
| positive | 82 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.21 | - |
| 0.0923 | 50 | 0.0894 | - |
| 0.1845 | 100 | 0.1356 | - |
| 0.2768 | 150 | 0.0014 | - |
| 0.3690 | 200 | 0.0004 | - |
| 0.4613 | 250 | 0.0005 | - |
| 0.5535 | 300 | 0.0003 | - |
| 0.6458 | 350 | 0.0003 | - |
| 0.7380 | 400 | 0.0004 | - |
| 0.8303 | 450 | 0.0001 | - |
| 0.9225 | 500 | 0.0002 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- spaCy: 3.7.3
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
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--> | {"library_name": "setfit", "tags": ["setfit", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "is the best French food you will find:It may be a bit packed on weekends, but the vibe is good and it is the best French food you will find in the area."}, {"text": "knew what the specials were.:Whem asked, we had to ask more detailed questions so that we knew what the specials were."}, {"text": "all out wow dining experience.:Go here for a romantic dinner but not for an all out wow dining experience."}, {"text": "vibe, the owner is super friendly:Best of all is the warm vibe, the owner is super friendly and service is fast."}, {"text": "all of the dishes are excellent.:The menu is limited but almost all of the dishes are excellent."}], "pipeline_tag": "text-classification", "inference": false, "base_model": "sentence-transformers/paraphrase-mpnet-base-v2"} | text-classification | tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity | [
"setfit",
"safetensors",
"mpnet",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"region:us"
] | 2024-02-09T06:08:06+00:00 | [
"2209.11055"
] | [] | TAGS
#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us
| SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
=========================================================================
This is a SetFit model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of classifying aspect polarities.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. Use a SetFit model to filter these possible aspect span candidates.
3. Use this SetFit model to classify the filtered aspect span candidates.
Model Details
-------------
### Model Description
* Model Type: SetFit
* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
* Classification head: a LogisticRegression instance
* spaCy Model: en\_core\_web\_lg
* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect
* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity
* Maximum Sequence Length: 512 tokens
* Number of Classes: 4 classes
### Model Sources
* Repository: SetFit on GitHub
* Paper: Efficient Few-Shot Learning Without Prompts
* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
### Model Labels
Uses
----
### Direct Use for Inference
First install the SetFit library:
Then you can load this model and run inference.
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* batch\_size: (16, 2)
* num\_epochs: (1, 16)
* max\_steps: -1
* sampling\_strategy: oversampling
* body\_learning\_rate: (2e-05, 1e-05)
* head\_learning\_rate: 0.01
* loss: CosineSimilarityLoss
* distance\_metric: cosine\_distance
* margin: 0.25
* end\_to\_end: False
* use\_amp: False
* warmup\_proportion: 0.1
* seed: 42
* eval\_max\_steps: -1
* load\_best\_model\_at\_end: False
### Training Results
### Framework Versions
* Python: 3.10.12
* SetFit: 1.0.3
* Sentence Transformers: 2.3.1
* spaCy: 3.7.3
* Transformers: 4.35.2
* PyTorch: 2.1.0+cu121
* Datasets: 2.16.1
* Tokenizers: 0.15.1
### BibTeX
| [
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* spaCy: 3.7.3\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
"TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us \n",
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* spaCy: 3.7.3\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
71,
151,
52,
7,
31,
7,
168,
4,
65,
6
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"passage: TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect\n* SetFitABSA Polarity Model: tvjoseph/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False### Training Results"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).
## Model description
- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
- **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org
## Performance
At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks:
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|-------------|-----|----|---------------|--------------|
| StableLM-Tuned-α | 7B| dSFT |2.75| -|
| MPT-Chat | 7B |dSFT |5.42| -|
| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
| Mistral-Instructv0.1 | 7B| - | 6.84 |-|
| Zephyr-7b-α |7B| dDPO| 6.88| -|
| **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** |
| Falcon-Instruct | 40B |dSFT |5.17 |45.71|
| Guanaco | 65B | SFT |6.41| 71.80|
| Llama2-Chat | 70B |RLHF |6.86| 92.66|
| Vicuna v1.3 | 33B |dSFT |7.12 |88.99|
| WizardLM v1.0 | 70B |dSFT |7.71 |-|
| Xwin-LM v0.1 | 70B |dPPO |- |95.57|
| GPT-3.5-turbo | - |RLHF |7.94 |89.37|
| Claude 2 | - |RLHF |8.06| 91.36|
| GPT-4 | -| RLHF |8.99| 95.28|
In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:
However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities.
You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
## Training and evaluation data
During DPO training, this model achieves the following results on the evaluation set:
- Loss: 0.7496
- Rewards/chosen: -4.5221
- Rewards/rejected: -8.3184
- Rewards/accuracies: 0.7812
- Rewards/margins: 3.7963
- Logps/rejected: -340.1541
- Logps/chosen: -299.4561
- Logits/rejected: -2.3081
- Logits/chosen: -2.3531
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
The table below shows the full set of DPO training metrics:
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 |
| 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 |
| 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 |
| 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 |
| 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 |
| 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 |
| 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 |
| 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 |
| 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 |
| 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 |
| 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 |
| 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 |
| 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 |
| 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 |
| 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 |
| 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 |
| 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 |
| 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 |
| 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 |
| 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 |
| 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 |
| 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 |
| 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 |
| 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 |
| 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 |
| 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 |
| 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 |
| 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 |
| 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 |
| 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 |
| 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 |
| 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 |
| 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 |
| 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 |
| 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 |
| 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 |
| 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 |
| 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 |
| 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 |
| 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 |
| 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 |
| 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 |
| 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 |
| 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 |
| 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 |
| 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 |
| 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 |
| 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 |
| 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 |
| 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 |
| 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 |
| 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 |
| 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 |
| 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 |
| 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 |
| 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 |
| 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 |
| 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.14.0
## Citation
If you find Zephyr-7B-β is useful in your work, please cite it with:
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 52.15 |
| ARC (25-shot) | 62.03 |
| HellaSwag (10-shot) | 84.36 |
| MMLU (5-shot) | 61.07 |
| TruthfulQA (0-shot) | 57.45 |
| Winogrande (5-shot) | 77.74 |
| GSM8K (5-shot) | 12.74 |
| DROP (3-shot) | 9.66 | | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k", "HuggingFaceH4/ultrafeedback_binarized"], "base_model": "mistralai/Mistral-7B-v0.1", "widget": [{"text": "<|system|>\nYou are a pirate chatbot who always responds with Arr!</s>\n<|user|>\nThere's a llama on my lawn, how can I get rid of him?</s>\n<|assistant|>\n", "output": {"text": "Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr!"}}], "pipeline_tag": "text-generation", "model-index": [{"name": "zephyr-7b-beta", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 62.03071672354948, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 84.35570603465445, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Drop (3-Shot)", "type": "drop", "split": "validation", "args": {"num_few_shot": 3}}, "metrics": [{"type": "f1", "value": 9.66243708053691, "name": "f1 score"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 57.44916942762855}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 12.736921910538287, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 61.07, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 77.7426992896606, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AlpacaEval", "type": "tatsu-lab/alpaca_eval"}, "metrics": [{"type": "unknown", "value": 0.906, "name": "win rate"}], "source": {"url": "https://tatsu-lab.github.io/alpaca_eval/"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MT-Bench", "type": "unknown"}, "metrics": [{"type": "unknown", "value": 7.34, "name": "score"}], "source": {"url": "https://huggingface.co/spaces/lmsys/mt-bench"}}]}]} | text-generation | traversaal-ai/zephyr-7b-beta-5.0bpw-exl2 | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"arxiv:2305.18290",
"arxiv:2310.16944",
"base_model:mistralai/Mistral-7B-v0.1",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:09:35+00:00 | [
"2305.18290",
"2310.16944"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #generated_from_trainer #conversational #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #arxiv-2305.18290 #arxiv-2310.16944 #base_model-mistralai/Mistral-7B-v0.1 #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Model Card for Zephyr 7B β
==========================
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the technical report.
Model description
-----------------
* Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
* Language(s) (NLP): Primarily English
* License: MIT
* Finetuned from model: mistralai/Mistral-7B-v0.1
### Model Sources
* Repository: URL
* Demo: URL
* Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: URL
Performance
-----------
At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks:
In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:
!image/png
However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.
Intended uses & limitations
---------------------------
The model was initially fine-tuned on a filtered and preprocessed of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.
You can find the datasets used for training Zephyr-7B-β here
Here's how you can run the model using the 'pipeline()' function from Transformers:
Bias, Risks, and Limitations
----------------------------
Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base model ('mistralai/Mistral-7B-v0.1'), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.
Training and evaluation data
----------------------------
During DPO training, this model achieves the following results on the evaluation set:
* Loss: 0.7496
* Rewards/chosen: -4.5221
* Rewards/rejected: -8.3184
* Rewards/accuracies: 0.7812
* Rewards/margins: 3.7963
* Logps/rejected: -340.1541
* Logps/chosen: -299.4561
* Logits/rejected: -2.3081
* Logits/chosen: -2.3531
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-07
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 16
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0
### Training results
The table below shows the full set of DPO training metrics:
### Framework versions
* Transformers 4.35.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.14.0
If you find Zephyr-7B-β is useful in your work, please cite it with:
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
| [
"### Model Sources\n\n\n* Repository: URL\n* Demo: URL\n* Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: URL\n\n\nPerformance\n-----------\n\n\nAt the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks:\n\n\n\nIn particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:\n\n\n!image/png\n\n\nHowever, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.\n\n\nIntended uses & limitations\n---------------------------\n\n\nThe model was initially fine-tuned on a filtered and preprocessed of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.\nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.\n\n\nYou can find the datasets used for training Zephyr-7B-β here\n\n\nHere's how you can run the model using the 'pipeline()' function from Transformers:\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nZephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).\nIt is also unknown what the size and composition of the corpus was used to train the base model ('mistralai/Mistral-7B-v0.1'), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.\n\n\nTraining and evaluation data\n----------------------------\n\n\nDuring DPO training, this model achieves the following results on the evaluation set:\n\n\n* Loss: 0.7496\n* Rewards/chosen: -4.5221\n* Rewards/rejected: -8.3184\n* Rewards/accuracies: 0.7812\n* Rewards/margins: 3.7963\n* Logps/rejected: -340.1541\n* Logps/chosen: -299.4561\n* Logits/rejected: -2.3081\n* Logits/chosen: -2.3531",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3.0",
"### Training results\n\n\nThe table below shows the full set of DPO training metrics:",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.12.0\n* Tokenizers 0.14.0\n\n\nIf you find Zephyr-7B-β is useful in your work, please cite it with:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #generated_from_trainer #conversational #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #arxiv-2305.18290 #arxiv-2310.16944 #base_model-mistralai/Mistral-7B-v0.1 #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model Sources\n\n\n* Repository: URL\n* Demo: URL\n* Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: URL\n\n\nPerformance\n-----------\n\n\nAt the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks:\n\n\n\nIn particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:\n\n\n!image/png\n\n\nHowever, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.\n\n\nIntended uses & limitations\n---------------------------\n\n\nThe model was initially fine-tuned on a filtered and preprocessed of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.\nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.\n\n\nYou can find the datasets used for training Zephyr-7B-β here\n\n\nHere's how you can run the model using the 'pipeline()' function from Transformers:\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nZephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).\nIt is also unknown what the size and composition of the corpus was used to train the base model ('mistralai/Mistral-7B-v0.1'), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.\n\n\nTraining and evaluation data\n----------------------------\n\n\nDuring DPO training, this model achieves the following results on the evaluation set:\n\n\n* Loss: 0.7496\n* Rewards/chosen: -4.5221\n* Rewards/rejected: -8.3184\n* Rewards/accuracies: 0.7812\n* Rewards/margins: 3.7963\n* Logps/rejected: -340.1541\n* Logps/chosen: -299.4561\n* Logits/rejected: -2.3081\n* Logits/chosen: -2.3531",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3.0",
"### Training results\n\n\nThe table below shows the full set of DPO training metrics:",
"### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.12.0\n* Tokenizers 0.14.0\n\n\nIf you find Zephyr-7B-β is useful in your work, please cite it with:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
141,
595,
166,
18,
77
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #generated_from_trainer #conversational #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #arxiv-2305.18290 #arxiv-2310.16944 #base_model-mistralai/Mistral-7B-v0.1 #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | diffusers |
# URPM [LEGACY Version] API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "urpm-legacy-version"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/urpm-legacy-version)
Model link: [View model](https://modelslab.com/models/urpm-legacy-version)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "urpm-legacy-version",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | text-to-image | stablediffusionapi/urpm-legacy-version | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-09T06:15:24+00:00 | [] | [] | TAGS
#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# URPM [LEGACY Version] API Inference
!generated from URL
## Get API Key
Get API key from ModelsLab API, No Payment needed.
Replace Key in below code, change model_id to "urpm-legacy-version"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "urpm-legacy-version",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# URPM [LEGACY Version] API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"urpm-legacy-version\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"urpm-legacy-version\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# URPM [LEGACY Version] API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"urpm-legacy-version\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"urpm-legacy-version\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
70,
18,
552
] | [
"passage: TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# URPM [LEGACY Version] API Inference\n\n!generated from URL"
] | [
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null | null | transformers |
# Vision Alignment for google/siglip-base-patch16-384 + mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v3
WanDB at https://wandb.ai/huseinzol05/vision-alignment-tinyllama?workspace=user-huseinzol05 | {"library_name": "transformers", "tags": []} | null | mesolitica/malaysian-tinyllama-1.1b-siglip-base-384-vision-alignment | [
"transformers",
"safetensors",
"mm_llms",
"endpoints_compatible",
"region:us"
] | 2024-02-09T06:18:39+00:00 | [] | [] | TAGS
#transformers #safetensors #mm_llms #endpoints_compatible #region-us
|
# Vision Alignment for google/siglip-base-patch16-384 + mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v3
WanDB at URL | [
"# Vision Alignment for google/siglip-base-patch16-384 + mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v3\n\nWanDB at URL"
] | [
"TAGS\n#transformers #safetensors #mm_llms #endpoints_compatible #region-us \n",
"# Vision Alignment for google/siglip-base-patch16-384 + mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v3\n\nWanDB at URL"
] | [
27,
46
] | [
"passage: TAGS\n#transformers #safetensors #mm_llms #endpoints_compatible #region-us \n# Vision Alignment for google/siglip-base-patch16-384 + mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v3\n\nWanDB at URL"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# billsum_8991_t5-v1_1-large
This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6520
- Rouge1: 0.174
- Rouge2: 0.0822
- Rougel: 0.1434
- Rougelsum: 0.1433
- Gen Len: 18.9297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.8798 | 6.75 | 500 | 2.6520 | 0.174 | 0.0822 | 0.1434 | 0.1433 | 18.9297 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/t5-v1_1-large", "model-index": [{"name": "billsum_8991_t5-v1_1-large", "results": []}]} | text2text-generation | ryusangwon/billsum_8991_t5-v1_1-large | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/t5-v1_1-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:19:12+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| billsum\_8991\_t5-v1\_1-large
=============================
This model is a fine-tuned version of google/t5-v1\_1-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6520
* Rouge1: 0.174
* Rouge2: 0.0822
* Rougel: 0.1434
* Rougelsum: 0.1433
* Gen Len: 18.9297
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.0.1+cu117
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
79,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-england-0209-parallel_attempt-avatar
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7083
- Wer: 0.5201
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1227
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9994 | 1.0 | 1227 | 1.2574 | 0.9228 |
| 0.9949 | 2.0 | 2454 | 0.8205 | 0.7387 |
| 0.7541 | 3.0 | 3681 | 0.7099 | 0.6686 |
| 0.6226 | 4.0 | 4908 | 0.6457 | 0.6329 |
| 0.5317 | 5.0 | 6135 | 0.6006 | 0.5946 |
| 0.46 | 6.0 | 7362 | 0.5881 | 0.5734 |
| 0.4012 | 7.0 | 8589 | 0.5935 | 0.5584 |
| 0.3499 | 8.0 | 9816 | 0.5885 | 0.5508 |
| 0.3066 | 9.0 | 11043 | 0.5907 | 0.5415 |
| 0.2675 | 10.0 | 12270 | 0.5974 | 0.5352 |
| 0.2332 | 11.0 | 13497 | 0.6124 | 0.5340 |
| 0.2027 | 12.0 | 14724 | 0.6387 | 0.5279 |
| 0.1774 | 13.0 | 15951 | 0.6570 | 0.5245 |
| 0.1562 | 14.0 | 17178 | 0.6867 | 0.5218 |
| 0.1396 | 15.0 | 18405 | 0.7083 | 0.5201 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.14.7
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0209-parallel_attempt-avatar", "results": []}]} | automatic-speech-recognition | Lin25/wav2vec2-300m-england-0209-parallel_attempt-avatar | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T06:23:36+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-england-0209-parallel\_attempt-avatar
===================================================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7083
* Wer: 0.5201
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1227
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.14.7
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
80,
159,
4,
40
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.921
- F1: 0.9209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3423 | 0.9035 | 0.9022 |
| No log | 2.0 | 500 | 0.2242 | 0.921 | 0.9209 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.921, "name": "Accuracy"}, {"type": "f1", "value": 0.9209418048779289, "name": "F1"}]}]}]} | text-classification | kw9101/distilbert-base-uncased-finetuned-emotion | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T06:26:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2242
* Accuracy: 0.921
* F1: 0.9209
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
82,
98,
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m-lora-1.57M-squad-model1
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-lora-1.57M-squad-model1", "results": []}]} | question-answering | varun-v-rao/opt-350m-lora-1.57M-squad-model1 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:facebook/opt-350m",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:27:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-lora-1.57M-squad-model1
This model is a fine-tuned version of facebook/opt-350m on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# opt-350m-lora-1.57M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-lora-1.57M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-350m-lora-1.57M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 1\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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null | null | transformers |
# Crystalmistral-13b
Crystalmistral-13b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B)
* [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: eren23/dpo-binarized-NeuralTrix-7B
layer_range: [0, 32]
- sources:
- model: eren23/dpo-binarized-NeuralTrix-7B
layer_range: [0, 32]
merge_method: passthrough
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Crystalcareai/Crystalmistral-13b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "eren23/dpo-binarized-NeuralTrix-7B", "eren23/dpo-binarized-NeuralTrix-7B"], "base_model": ["eren23/dpo-binarized-NeuralTrix-7B", "eren23/dpo-binarized-NeuralTrix-7B"]} | text-generation | Crystalcareai/CrystalMistral-14b | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"eren23/dpo-binarized-NeuralTrix-7B",
"base_model:eren23/dpo-binarized-NeuralTrix-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:29:24+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #base_model-eren23/dpo-binarized-NeuralTrix-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Crystalmistral-13b
Crystalmistral-13b is a merge of the following models using LazyMergekit:
* eren23/dpo-binarized-NeuralTrix-7B
* eren23/dpo-binarized-NeuralTrix-7B
## Configuration
## Usage
| [
"# Crystalmistral-13b\n\nCrystalmistral-13b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* eren23/dpo-binarized-NeuralTrix-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #base_model-eren23/dpo-binarized-NeuralTrix-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Crystalmistral-13b\n\nCrystalmistral-13b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* eren23/dpo-binarized-NeuralTrix-7B",
"## Configuration",
"## Usage"
] | [
98,
61,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #eren23/dpo-binarized-NeuralTrix-7B #base_model-eren23/dpo-binarized-NeuralTrix-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Crystalmistral-13b\n\nCrystalmistral-13b is a merge of the following models using LazyMergekit:\n* eren23/dpo-binarized-NeuralTrix-7B\n* eren23/dpo-binarized-NeuralTrix-7B## Configuration## Usage"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | jeevana/G8_mistral7b_qlora_1211_v09feb | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:32:21+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
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BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "269.00 +/- 23.40", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | lgarma/LunarLander | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T06:38:33+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert_0209_2
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1581
- Wer: 0.3363
- Cer: 0.1165
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 11.5147 | 1.0 | 60 | 9.5819 | 0.9937 | 0.9981 |
| 6.4005 | 2.0 | 120 | 6.0341 | 0.9937 | 0.9981 |
| 4.6809 | 3.0 | 180 | 4.4257 | 0.9937 | 0.9981 |
| 3.4233 | 4.0 | 240 | 3.2761 | 0.9937 | 0.9981 |
| 2.9519 | 5.0 | 300 | 2.8505 | 0.9937 | 0.9981 |
| 2.1522 | 6.0 | 360 | 1.9329 | 1.0 | 0.8908 |
| 1.3607 | 7.0 | 420 | 1.2185 | 1.0 | 0.6644 |
| 1.1517 | 8.0 | 480 | 1.0058 | 0.9925 | 0.5520 |
| 0.842 | 9.0 | 540 | 0.7791 | 0.8197 | 0.4785 |
| 0.7568 | 10.0 | 600 | 0.7093 | 0.8111 | 0.4786 |
| 0.7831 | 11.0 | 660 | 0.6805 | 0.8111 | 0.5536 |
| 0.6818 | 12.0 | 720 | 0.6399 | 0.8029 | 0.4912 |
| 0.6755 | 13.0 | 780 | 0.6224 | 0.7996 | 0.4944 |
| 0.6378 | 14.0 | 840 | 0.5772 | 0.7428 | 0.3915 |
| 0.6015 | 15.0 | 900 | 0.5665 | 0.7077 | 0.3497 |
| 0.5379 | 16.0 | 960 | 0.4916 | 0.6823 | 0.2954 |
| 0.4724 | 17.0 | 1020 | 0.4250 | 0.7003 | 0.3300 |
| 1.0444 | 18.0 | 1080 | 0.4027 | 0.6596 | 0.3167 |
| 0.4322 | 19.0 | 1140 | 0.3760 | 0.6185 | 0.2732 |
| 0.3998 | 20.0 | 1200 | 0.3369 | 0.5640 | 0.2241 |
| 0.3817 | 21.0 | 1260 | 0.3492 | 0.5733 | 0.2630 |
| 0.4871 | 22.0 | 1320 | 0.3284 | 0.5961 | 0.2648 |
| 0.3341 | 23.0 | 1380 | 0.2799 | 0.5032 | 0.2151 |
| 0.3036 | 24.0 | 1440 | 0.2609 | 0.4871 | 0.2098 |
| 0.2866 | 25.0 | 1500 | 0.2421 | 0.4461 | 0.1762 |
| 0.2522 | 26.0 | 1560 | 0.2021 | 0.3904 | 0.1435 |
| 0.2507 | 27.0 | 1620 | 0.1979 | 0.3856 | 0.1383 |
| 0.2446 | 28.0 | 1680 | 0.1721 | 0.3531 | 0.1268 |
| 0.2023 | 29.0 | 1740 | 0.1641 | 0.3501 | 0.1218 |
| 0.2042 | 30.0 | 1800 | 0.1581 | 0.3363 | 0.1165 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_0209_2", "results": []}]} | automatic-speech-recognition | tndklab/hubert_0209_2 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T06:39:07+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
| hubert\_0209\_2
===============
This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1581
* Wer: 0.3363
* Cer: 0.1165
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LazarusNLP/IndoNanoT5-base-XPersona
This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the indonlg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8372
- Bleu: 4.0669
- Sacrebleu: 4.0669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Sacrebleu |
|:-------------:|:-----:|:------:|:---------------:|:------:|:---------:|
| 1.9872 | 1.0 | 15516 | 1.8482 | 3.7015 | 3.7015 |
| 1.888 | 2.0 | 31032 | 1.8434 | 4.0409 | 4.0409 |
| 1.8207 | 3.0 | 46548 | 1.8347 | 4.1239 | 4.1239 |
| 1.7716 | 4.0 | 62064 | 1.8340 | 4.3231 | 4.3231 |
| 1.6948 | 5.0 | 77580 | 1.8443 | 4.4283 | 4.4283 |
| 1.6442 | 6.0 | 93096 | 1.8563 | 4.5338 | 4.5338 |
| 1.5856 | 7.0 | 108612 | 1.8782 | 4.3033 | 4.3033 |
| 1.5451 | 8.0 | 124128 | 1.8930 | 4.3286 | 4.3286 |
| 1.5056 | 9.0 | 139644 | 1.9207 | 4.2773 | 4.2773 |
| 1.446 | 10.0 | 155160 | 1.9406 | 4.0629 | 4.0629 |
| 1.406 | 11.0 | 170676 | 1.9636 | 4.1382 | 4.1382 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"language": ["ind"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["GEM/indonlg"], "metrics": ["bleu", "sacrebleu"], "base_model": "LazarusNLP/IndoNanoT5-base", "model-index": [{"name": "IndoNanoT5-base-XPersona", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "indonlg", "type": "indonlg", "config": "xpersona", "split": "test", "args": "xpersona"}, "metrics": [{"type": "bleu", "value": 4.0669, "name": "Bleu"}, {"type": "sacrebleu", "value": 4.0669, "name": "Sacrebleu"}]}]}]} | text2text-generation | LazarusNLP/IndoNanoT5-base-XPersona | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"ind",
"dataset:GEM/indonlg",
"base_model:LazarusNLP/IndoNanoT5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T06:40:44+00:00 | [] | [
"ind"
] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #ind #dataset-GEM/indonlg #base_model-LazarusNLP/IndoNanoT5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LazarusNLP/IndoNanoT5-base-XPersona
===================================
This model is a fine-tuned version of LazarusNLP/IndoNanoT5-base on the indonlg dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8372
* Bleu: 4.0669
* Sacrebleu: 4.0669
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
101,
98,
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33
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"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #ind #dataset-GEM/indonlg #base_model-LazarusNLP/IndoNanoT5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | matsudatkm/finetune_base-freezeEncoder | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T06:43:39+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## How to Get Started with the Model
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## Training Details
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## Evaluation
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"passage: TAGS\n#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "KnutJaegersberg/Qwen-14B-Llamafied"} | null | lmg-anon/vntl-qwen-14b-v0.1-qlora | [
"peft",
"safetensors",
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] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-KnutJaegersberg/Qwen-14B-Llamafied #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JinJung/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8490
- Validation Loss: 1.7203
- Train Accuracy: 0.793
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8490 | 1.7203 | 0.793 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "JinJung/food_classifier", "results": []}]} | image-classification | JinJung/food_classifier | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:01:57+00:00 | [] | [] | TAGS
#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| JinJung/food\_classifier
========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8490
* Validation Loss: 1.7203
* Train Accuracy: 0.793
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 4000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
73,
226,
4,
31
] | [
"passage: TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
# CRIA v1.3
💡 [Article](https://walterteng.com/cria) |
💻 [Github](https://github.com/davzoku/cria) |
📔 Colab [1](https://colab.research.google.com/drive/1rYTs3qWJerrYwihf1j0f00cnzzcpAfYe),[2](https://colab.research.google.com/drive/1Wjs2I1VHjs6zT_GE42iEXsLtYh6VqiJU)
## What is CRIA?
> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
<p align="center">
<img src="https://raw.githubusercontent.com/davzoku/cria/main/assets/icon-512x512.png" width="300" height="300" alt="Cria Logo"> <br>
<i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
</p>
The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as Next.js.
Specifically, we have fine-tuned the `llama-2-7b-chat-hf` model with QLoRA (4-bit precision) using the [mlabonne/CodeLlama-2-20k](https://huggingface.co/datasets/mlabonne/CodeLlama-2-20k) dataset. This fine-tuned model serves as the backbone for the [CRIA chat](https://chat.walterteng.com) platform.
## 📦 Model Release
CRIA v1.3 comes with several variants.
- [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3): Merged Model
- [davzoku/cria-llama2-7b-v1.3-GGML](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGML): Quantized Merged Model
- [davzoku/cria-llama2-7b-v1.3_peft](https://huggingface.co/davzoku/cria-llama2-7b-v1.3_peft): PEFT adapter
- [davzoku/cria-llama2-7b-v1.3-GGUF](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGUF): GGUF Format
- converted from ggml vq4_0 using `python3 convert-llama-ggml-to-gguf.py -i ../text-generation-webui/models/cria/cria-llama2-7b-v1.3.ggmlv3.q4_0.bin -o cria-llama2-7b-v1.3.gguf`
- [davzoku/cria-llama2-7b-v1.3-mlx](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-mlx): MLX Format in FP16
- Converted from [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3) using [mlx-examples](https://github.com/davzoku/mlx-examples). see [NOTES.md](https://github.com/davzoku/mlx-examples/blob/cria/notes/NOTES.md)
- [davzoku/cria-llama2-7b-v1.3-q4-mlx](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-q4-mlx): MLX Format in Q4
- Converted from [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3) using [mlx-examples](https://github.com/davzoku/mlx-examples). see [NOTES.md](https://github.com/davzoku/mlx-examples/blob/cria/notes/NOTES.md)
## 🔧 Training
It was trained on a Google Colab notebook with a T4 GPU and high RAM.
### Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
## 💻 Usage
This model was converted to MLX format from [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3).
### Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("davzoku/cria-llama2-7b-v1.3-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
### Original Usage
```python
# pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "davzoku/cria-llama2-7b-v1.3"
prompt = "What is a cria?"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
f'<s>[INST] {prompt} [/INST]',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## References
We'd like to thank:
- [mlabonne](https://huggingface.co/mlabonne) for his article and resources on implementation of instruction tuning
- [TheBloke](https://huggingface.co/TheBloke) for his script for LLM quantization.
| {"language": "en", "license": "llama2", "tags": ["llama-2"], "datasets": ["mlabonne/CodeLlama-2-20k"], "inference": false, "model_type": "llama", "pipeline_tag": "text-generation"} | text-generation | davzoku/cria-llama2-7b-v1.3-mlx | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"en",
"dataset:mlabonne/CodeLlama-2-20k",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:01:59+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us
|
# CRIA v1.3
Article |
Github |
Colab 1,2
## What is CRIA?
> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
<p align="center">
<img src="URL width="300" height="300" alt="Cria Logo"> <br>
<i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
</p>
The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.
Specifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.
## Model Release
CRIA v1.3 comes with several variants.
- davzoku/cria-llama2-7b-v1.3: Merged Model
- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model
- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter
- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format
- converted from ggml vq4_0 using 'python3 URL -i ../text-generation-webui/models/cria/cria-llama2-7b-v1.3.ggmlv3.q4_0.bin -o cria-llama2-7b-v1.3.gguf'
- davzoku/cria-llama2-7b-v1.3-mlx: MLX Format in FP16
- Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL
- davzoku/cria-llama2-7b-v1.3-q4-mlx: MLX Format in Q4
- Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL
## Training
It was trained on a Google Colab notebook with a T4 GPU and high RAM.
### Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
## Usage
This model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.
### Use with mlx
### Original Usage
## References
We'd like to thank:
- mlabonne for his article and resources on implementation of instruction tuning
- TheBloke for his script for LLM quantization.
| [
"# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2",
"## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.",
"## Model Release\n\nCRIA v1.3 comes with several variants.\n\n- davzoku/cria-llama2-7b-v1.3: Merged Model\n- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model\n- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter\n- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format\n - converted from ggml vq4_0 using 'python3 URL -i ../text-generation-webui/models/cria/cria-llama2-7b-v1.3.ggmlv3.q4_0.bin -o cria-llama2-7b-v1.3.gguf'\n- davzoku/cria-llama2-7b-v1.3-mlx: MLX Format in FP16\n - Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL\n- davzoku/cria-llama2-7b-v1.3-q4-mlx: MLX Format in Q4\n - Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL",
"## Training\n\nIt was trained on a Google Colab notebook with a T4 GPU and high RAM.",
"### Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n- PEFT 0.4.0",
"## Usage\n\nThis model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.",
"### Use with mlx",
"### Original Usage",
"## References\n\nWe'd like to thank:\n\n- mlabonne for his article and resources on implementation of instruction tuning\n- TheBloke for his script for LLM quantization."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n",
"# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2",
"## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.",
"## Model Release\n\nCRIA v1.3 comes with several variants.\n\n- davzoku/cria-llama2-7b-v1.3: Merged Model\n- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model\n- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter\n- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format\n - converted from ggml vq4_0 using 'python3 URL -i ../text-generation-webui/models/cria/cria-llama2-7b-v1.3.ggmlv3.q4_0.bin -o cria-llama2-7b-v1.3.gguf'\n- davzoku/cria-llama2-7b-v1.3-mlx: MLX Format in FP16\n - Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL\n- davzoku/cria-llama2-7b-v1.3-q4-mlx: MLX Format in Q4\n - Converted from davzoku/cria-llama2-7b-v1.3 using mlx-examples. see URL",
"## Training\n\nIt was trained on a Google Colab notebook with a T4 GPU and high RAM.",
"### Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n- PEFT 0.4.0",
"## Usage\n\nThis model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.",
"### Use with mlx",
"### Original Usage",
"## References\n\nWe'd like to thank:\n\n- mlabonne for his article and resources on implementation of instruction tuning\n- TheBloke for his script for LLM quantization."
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform."
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null | null | transformers | <center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development
DevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :
* [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
* [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2)
A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.
## Configuration
```yaml
base_model: codellama/CodeLlama-7b-Instruct-hf
experts:
- source_model: deepseek-ai/deepseek-coder-6.7b-instruct
positive_prompts:
- "python"
- "javascript"
- "java"
- source_model: defog/sqlcoder-7b-2
positive_prompts:
- "SQL"
```
## Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/DevPearl-2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development},
year = {2024}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/DevPearl-2x7B}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | {"language": ["en"], "license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["moe", "merge", "mergekit", "lazymergekit", "deepseek-ai/deepseek-coder-6.7b-instruct", "defog/sqlcoder-7b-2", "Python", "Javascript", "sql"], "base_model": ["deepseek-ai/deepseek-coder-6.7b-instruct", "defog/sqlcoder-7b-2"], "pipeline_tag": "text-generation"} | text-generation | louisbrulenaudet/DevPearl-2x7B | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"mergekit",
"lazymergekit",
"deepseek-ai/deepseek-coder-6.7b-instruct",
"defog/sqlcoder-7b-2",
"Python",
"Javascript",
"sql",
"en",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:defog/sqlcoder-7b-2",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:04:46+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #merge #mergekit #lazymergekit #deepseek-ai/deepseek-coder-6.7b-instruct #defog/sqlcoder-7b-2 #Python #Javascript #sql #en #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #base_model-defog/sqlcoder-7b-2 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <center><img src='https://i.URL width='450px'></center>
# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development
DevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :
* deepseek-ai/deepseek-coder-6.7b-instruct
* defog/sqlcoder-7b-2
A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.
## Configuration
## Usage
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
## Feedback
If you have any feedback, please reach out at louisbrulenaudet@URL. | [
"# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development\n\nDevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :\n* deepseek-ai/deepseek-coder-6.7b-instruct\n* defog/sqlcoder-7b-2\n\nA Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.",
"## Configuration",
"## Usage",
"## Citing & Authors\n\nIf you use this code in your research, please use the following BibTeX entry.",
"## Feedback\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL."
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"# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development\n\nDevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :\n* deepseek-ai/deepseek-coder-6.7b-instruct\n* defog/sqlcoder-7b-2\n\nA Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.",
"## Configuration",
"## Usage",
"## Citing & Authors\n\nIf you use this code in your research, please use the following BibTeX entry.",
"## Feedback\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL."
] | [
154,
190,
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"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #moe #merge #mergekit #lazymergekit #deepseek-ai/deepseek-coder-6.7b-instruct #defog/sqlcoder-7b-2 #Python #Javascript #sql #en #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #base_model-defog/sqlcoder-7b-2 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development\n\nDevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :\n* deepseek-ai/deepseek-coder-6.7b-instruct\n* defog/sqlcoder-7b-2\n\nA Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.## Configuration## Usage## Citing & Authors\n\nIf you use this code in your research, please use the following BibTeX entry.## Feedback\n\nIf you have any feedback, please reach out at louisbrulenaudet@URL."
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null | null | diffusers | # qq
<Gallery />
## Model description
qqq
## Trigger words
You should use `qqq` to trigger the image generation.
## Download model
[Download](/lulu0711/we/tree/main) them in the Files & versions tab.
| {"license": "openrail", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "1", "parameters": {"negative_prompt": "2"}, "output": {"url": "images/\u5fae\u4fe1\u622a\u56fe_20240129181936.png"}}], "base_model": "brathief/wwoo_1000_lora", "instance_prompt": "qqq"} | text-to-image | lulu0711/we | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:brathief/wwoo_1000_lora",
"license:openrail",
"region:us"
] | 2024-02-09T07:16:20+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-brathief/wwoo_1000_lora #license-openrail #region-us
| # qq
<Gallery />
## Model description
qqq
## Trigger words
You should use 'qqq' to trigger the image generation.
## Download model
Download them in the Files & versions tab.
| [
"# qq\n\n<Gallery />",
"## Model description \n\nqqq",
"## Trigger words\n\nYou should use 'qqq' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-brathief/wwoo_1000_lora #license-openrail #region-us \n",
"# qq\n\n<Gallery />",
"## Model description \n\nqqq",
"## Trigger words\n\nYou should use 'qqq' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
57,
8,
5,
17,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-brathief/wwoo_1000_lora #license-openrail #region-us \n# qq\n\n<Gallery />## Model description \n\nqqq## Trigger words\n\nYou should use 'qqq' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab."
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null | null | transformers |
# CRIA v1.3
💡 [Article](https://walterteng.com/cria) |
💻 [Github](https://github.com/davzoku/cria) |
📔 Colab [1](https://colab.research.google.com/drive/1rYTs3qWJerrYwihf1j0f00cnzzcpAfYe),[2](https://colab.research.google.com/drive/1Wjs2I1VHjs6zT_GE42iEXsLtYh6VqiJU)
## What is CRIA?
> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
<p align="center">
<img src="https://raw.githubusercontent.com/davzoku/cria/main/assets/icon-512x512.png" width="300" height="300" alt="Cria Logo"> <br>
<i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
</p>
The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as Next.js.
Specifically, we have fine-tuned the `llama-2-7b-chat-hf` model with QLoRA (4-bit precision) using the [mlabonne/CodeLlama-2-20k](https://huggingface.co/datasets/mlabonne/CodeLlama-2-20k) dataset. This fine-tuned model serves as the backbone for the [CRIA chat](https://chat.walterteng.com) platform.
## 📦 Model Release
CRIA v1.3 comes with several variants.
- [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3): Merged Model
- [davzoku/cria-llama2-7b-v1.3-GGML](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGML): Quantized Merged Model
- [davzoku/cria-llama2-7b-v1.3_peft](https://huggingface.co/davzoku/cria-llama2-7b-v1.3_peft): PEFT adapter
- [davzoku/cria-llama2-7b-v1.3-GGUF](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGUF): GGUF Format
This model is converted from the q4_0 GGML version of CRIA v1.3 using the [llama.cpp's convert-llama-ggml-to-gguf.py](https://github.com/ggerganov/llama.cpp/commit/f23c0359a32871947169a044eb1dc4dbffd0f405#diff-ca59e5f600b44e9c3cdfed1ffd04677524dfd4c2f43d8b4bb19fdb013e277871) script
## 🔧 Training
It was trained on a Google Colab notebook with a T4 GPU and high RAM.
### Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
## 💻 Usage
This model was converted to MLX format from [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3).
### Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("davzoku/cria-llama2-7b-v1.3-q4-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
### Original Usage
```python
# pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "davzoku/cria-llama2-7b-v1.3"
prompt = "What is a cria?"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
f'<s>[INST] {prompt} [/INST]',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## References
We'd like to thank:
- [mlabonne](https://huggingface.co/mlabonne) for his article and resources on implementation of instruction tuning
- [TheBloke](https://huggingface.co/TheBloke) for his script for LLM quantization.
| {"language": "en", "license": "llama2", "tags": ["llama-2"], "datasets": ["mlabonne/CodeLlama-2-20k"], "inference": false, "model_type": "llama", "pipeline_tag": "text-generation"} | text-generation | davzoku/cria-llama2-7b-v1.3-q4-mlx | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"en",
"dataset:mlabonne/CodeLlama-2-20k",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:18:05+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us
|
# CRIA v1.3
Article |
Github |
Colab 1,2
## What is CRIA?
> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
<p align="center">
<img src="URL width="300" height="300" alt="Cria Logo"> <br>
<i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
</p>
The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.
Specifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.
## Model Release
CRIA v1.3 comes with several variants.
- davzoku/cria-llama2-7b-v1.3: Merged Model
- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model
- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter
- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format
This model is converted from the q4_0 GGML version of CRIA v1.3 using the URL's URL script
## Training
It was trained on a Google Colab notebook with a T4 GPU and high RAM.
### Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
## Usage
This model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.
### Use with mlx
### Original Usage
## References
We'd like to thank:
- mlabonne for his article and resources on implementation of instruction tuning
- TheBloke for his script for LLM quantization.
| [
"# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2",
"## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.",
"## Model Release\n\nCRIA v1.3 comes with several variants.\n\n- davzoku/cria-llama2-7b-v1.3: Merged Model\n- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model\n- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter\n- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format\n\nThis model is converted from the q4_0 GGML version of CRIA v1.3 using the URL's URL script",
"## Training\n\nIt was trained on a Google Colab notebook with a T4 GPU and high RAM.",
"### Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n- PEFT 0.4.0",
"## Usage\n\nThis model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.",
"### Use with mlx",
"### Original Usage",
"## References\n\nWe'd like to thank:\n\n- mlabonne for his article and resources on implementation of instruction tuning\n- TheBloke for his script for LLM quantization."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n",
"# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2",
"## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.",
"## Model Release\n\nCRIA v1.3 comes with several variants.\n\n- davzoku/cria-llama2-7b-v1.3: Merged Model\n- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model\n- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter\n- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format\n\nThis model is converted from the q4_0 GGML version of CRIA v1.3 using the URL's URL script",
"## Training\n\nIt was trained on a Google Colab notebook with a T4 GPU and high RAM.",
"### Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n- PEFT 0.4.0",
"## Usage\n\nThis model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.",
"### Use with mlx",
"### Original Usage",
"## References\n\nWe'd like to thank:\n\n- mlabonne for his article and resources on implementation of instruction tuning\n- TheBloke for his script for LLM quantization."
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# CRIA v1.3\n\n Article |\n Github |\n Colab 1,2## What is CRIA?\n\n> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"Cria Logo\"> <br>\n <i>or what ChatGPT suggests, <b>\"Crafting a Rapid prototype of an Intelligent llm App using open source resources\"</b>.</i>\n</p>\n\nThe initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as URL.\n\nSpecifically, we have fine-tuned the 'llama-2-7b-chat-hf' model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.## Model Release\n\nCRIA v1.3 comes with several variants.\n\n- davzoku/cria-llama2-7b-v1.3: Merged Model\n- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model\n- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter\n- davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format\n\nThis model is converted from the q4_0 GGML version of CRIA v1.3 using the URL's URL script## Training\n\nIt was trained on a Google Colab notebook with a T4 GPU and high RAM."
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null | null | null | Coming Soon After The Lunar New Year!
| {} | null | USAIL-HKUSTGZ/LLMLight-LightGPT | [
"region:us"
] | 2024-02-09T07:24:05+00:00 | [] | [] | TAGS
#region-us
| Coming Soon After The Lunar New Year!
| [] | [
"TAGS\n#region-us \n"
] | [
6
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null | null | transformers | # maid-yuzu-v7
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
I don't know anything about merges, so this may be a stupid method, but I was curious how the models would be merged if I took this approach.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
This model is a model that first merges Model [Orochi](https://huggingface.co/smelborp/MixtralOrochi8x7B) with Model [dolphin](https://huggingface.co/cognitivecomputations/dolphin-2.7-mixtral-8x7b) with a 0.15 SLERP option, and then merges Model [BagelMIsteryTour](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) with a 0.2 SLERP option based on the merged model.
### Models Merged
The following models were included in the merge:
* [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B)
* ../maid-yuzu-v7-base
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model:
model:
path: ../maid-yuzu-v7-base
dtype: bfloat16
merge_method: slerp
parameters:
t:
- value: 0.2
slices:
- sources:
- layer_range: [0, 32]
model:
model:
path: ../maid-yuzu-v7-base
- layer_range: [0, 32]
model:
model:
path: ycros/BagelMIsteryTour-v2-8x7B
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["ycros/BagelMIsteryTour-v2-8x7B", "smelborp/MixtralOrochi8x7B", "cognitivecomputations/dolphin-2.7-mixtral-8x7b"]} | text-generation | rhplus0831/maid-yuzu-v7 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"base_model:ycros/BagelMIsteryTour-v2-8x7B",
"base_model:smelborp/MixtralOrochi8x7B",
"base_model:cognitivecomputations/dolphin-2.7-mixtral-8x7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:25:04+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-ycros/BagelMIsteryTour-v2-8x7B #base_model-smelborp/MixtralOrochi8x7B #base_model-cognitivecomputations/dolphin-2.7-mixtral-8x7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # maid-yuzu-v7
This is a merge of pre-trained language models created using mergekit.
I don't know anything about merges, so this may be a stupid method, but I was curious how the models would be merged if I took this approach.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
This model is a model that first merges Model Orochi with Model dolphin with a 0.15 SLERP option, and then merges Model BagelMIsteryTour with a 0.2 SLERP option based on the merged model.
### Models Merged
The following models were included in the merge:
* ycros/BagelMIsteryTour-v2-8x7B
* ../maid-yuzu-v7-base
### Configuration
The following YAML configuration was used to produce this model:
| [
"# maid-yuzu-v7\n\nThis is a merge of pre-trained language models created using mergekit.\n\nI don't know anything about merges, so this may be a stupid method, but I was curious how the models would be merged if I took this approach.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.\n\nThis model is a model that first merges Model Orochi with Model dolphin with a 0.15 SLERP option, and then merges Model BagelMIsteryTour with a 0.2 SLERP option based on the merged model.",
"### Models Merged\n\nThe following models were included in the merge:\n* ycros/BagelMIsteryTour-v2-8x7B\n* ../maid-yuzu-v7-base",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-ycros/BagelMIsteryTour-v2-8x7B #base_model-smelborp/MixtralOrochi8x7B #base_model-cognitivecomputations/dolphin-2.7-mixtral-8x7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# maid-yuzu-v7\n\nThis is a merge of pre-trained language models created using mergekit.\n\nI don't know anything about merges, so this may be a stupid method, but I was curious how the models would be merged if I took this approach.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.\n\nThis model is a model that first merges Model Orochi with Model dolphin with a 0.15 SLERP option, and then merges Model BagelMIsteryTour with a 0.2 SLERP option based on the merged model.",
"### Models Merged\n\nThe following models were included in the merge:\n* ycros/BagelMIsteryTour-v2-8x7B\n* ../maid-yuzu-v7-base",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
119,
60,
4,
69,
44,
17
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #base_model-ycros/BagelMIsteryTour-v2-8x7B #base_model-smelborp/MixtralOrochi8x7B #base_model-cognitivecomputations/dolphin-2.7-mixtral-8x7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# maid-yuzu-v7\n\nThis is a merge of pre-trained language models created using mergekit.\n\nI don't know anything about merges, so this may be a stupid method, but I was curious how the models would be merged if I took this approach.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.\n\nThis model is a model that first merges Model Orochi with Model dolphin with a 0.15 SLERP option, and then merges Model BagelMIsteryTour with a 0.2 SLERP option based on the merged model.### Models Merged\n\nThe following models were included in the merge:\n* ycros/BagelMIsteryTour-v2-8x7B\n* ../maid-yuzu-v7-base### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | transformers | # Mistral-7B-Instruct-v0.1-8bit
Create model
```python
model_path = "mistralai/Mistral-7B-Instruct-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_8bit=True
)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, quantization_config=bnb_config, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
```
Load in pipeline
```python
text_generation_pipeline = transformers.pipeline(
model=model,
tokenizer=tokenizer,
task="text-generation",
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
repetition_penalty=1.1,
return_full_text=True,
max_new_tokens=100,
)
mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
text = "what is mistral?"
mistral_llm.invoke(text)
```
| {} | text-generation | dmitrybright/Mistral-7B-Instruct-v0.1-8bit | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | 2024-02-09T07:25:27+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
| # Mistral-7B-Instruct-v0.1-8bit
Create model
Load in pipeline
| [
"# Mistral-7B-Instruct-v0.1-8bit\n\nCreate model\n\n\nLoad in pipeline"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"# Mistral-7B-Instruct-v0.1-8bit\n\nCreate model\n\n\nLoad in pipeline"
] | [
51,
20
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n# Mistral-7B-Instruct-v0.1-8bit\n\nCreate model\n\n\nLoad in pipeline"
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null | null | diffusers |
# SDXL LoRA DreamBooth - sushipanda/saburi-dog-sdxl-lora-3
<Gallery />
## Model description
### These are sushipanda/saburi-dog-sdxl-lora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`saburi-dog-sdxl-lora-3.safetensors` here 💾](/sushipanda/saburi-dog-sdxl-lora-3/blob/main/saburi-dog-sdxl-lora-3.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:saburi-dog-sdxl-lora-3:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`saburi-dog-sdxl-lora-3_emb.safetensors` here 💾](/sushipanda/saburi-dog-sdxl-lora-3/blob/main/saburi-dog-sdxl-lora-3_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `saburi-dog-sdxl-lora-3_emb` to your prompt. For example, `A saburi-dog-sdxl-lora-3_emb dog`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('sushipanda/saburi-dog-sdxl-lora-3', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='sushipanda/saburi-dog-sdxl-lora-3', filename='saburi-dog-sdxl-lora-3_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A <s0><s1> dog').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/sushipanda/saburi-dog-sdxl-lora-3/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A <s0><s1> dog", "output": {"url": "image-0.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-1.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-2.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-3.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-4.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-5.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-6.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-7.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-8.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-9.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-10.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-11.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-12.png"}}, {"text": "A <s0><s1> dog", "output": {"url": "image-13.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A <s0><s1> dog"} | text-to-image | sushipanda/saburi-dog-sdxl-lora-3 | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2024-02-09T07:26:30+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - sushipanda/saburi-dog-sdxl-lora-3
<Gallery />
## Model description
### These are sushipanda/saburi-dog-sdxl-lora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- LoRA: download 'saburi-dog-sdxl-lora-3.safetensors' here .
- Place it on your 'models/Lora' folder.
- On AUTOMATIC1111, load the LoRA by adding '<lora:saburi-dog-sdxl-lora-3:1>' to your prompt. On ComfyUI just load it as a regular LoRA.
- *Embeddings*: download 'saburi-dog-sdxl-lora-3_emb.safetensors' here .
- Place it on it on your 'embeddings' folder
- Use it by adding 'saburi-dog-sdxl-lora-3_emb' to your prompt. For example, 'A saburi-dog-sdxl-lora-3_emb dog'
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the diffusers library
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept 'TOK' → use '<s0><s1>' in your prompt
## Details
All Files & versions.
The weights were trained using diffusers Advanced Dreambooth Training Script.
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| [
"# SDXL LoRA DreamBooth - sushipanda/saburi-dog-sdxl-lora-3\n\n<Gallery />",
"## Model description",
"### These are sushipanda/saburi-dog-sdxl-lora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.",
"## Download model",
"### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'saburi-dog-sdxl-lora-3.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:saburi-dog-sdxl-lora-3:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'saburi-dog-sdxl-lora-3_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'saburi-dog-sdxl-lora-3_emb' to your prompt. For example, 'A saburi-dog-sdxl-lora-3_emb dog'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)",
"## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt",
"## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix."
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - sushipanda/saburi-dog-sdxl-lora-3\n\n<Gallery />",
"## Model description",
"### These are sushipanda/saburi-dog-sdxl-lora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.",
"## Download model",
"### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'saburi-dog-sdxl-lora-3.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:saburi-dog-sdxl-lora-3:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'saburi-dog-sdxl-lora-3_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'saburi-dog-sdxl-lora-3_emb' to your prompt. For example, 'A saburi-dog-sdxl-lora-3_emb dog'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)",
"## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt",
"## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix."
] | [
82,
29,
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] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n# SDXL LoRA DreamBooth - sushipanda/saburi-dog-sdxl-lora-3\n\n<Gallery />## Model description### These are sushipanda/saburi-dog-sdxl-lora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.## Download model### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'saburi-dog-sdxl-lora-3.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:saburi-dog-sdxl-lora-3:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'saburi-dog-sdxl-lora-3_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'saburi-dog-sdxl-lora-3_emb' to your prompt. For example, 'A saburi-dog-sdxl-lora-3_emb dog'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert_0209_3
This model is a fine-tuned version of [sarulab-speech/hubert-base-jtube](https://huggingface.co/sarulab-speech/hubert-base-jtube) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0133
- Wer: 0.1798
- Cer: 0.0658
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 9.1252 | 1.0 | 60 | 5.9959 | 0.9241 | 0.9768 |
| 3.59 | 2.0 | 120 | 3.2191 | 0.9241 | 0.9768 |
| 2.9684 | 3.0 | 180 | 2.6923 | 0.9241 | 0.9768 |
| 2.1023 | 4.0 | 240 | 1.7857 | 1.0 | 0.8262 |
| 1.2344 | 5.0 | 300 | 1.0982 | 1.0 | 0.5074 |
| 0.9809 | 6.0 | 360 | 0.8163 | 0.8079 | 0.4565 |
| 0.8071 | 7.0 | 420 | 0.7071 | 0.8094 | 0.4519 |
| 0.7241 | 8.0 | 480 | 0.6707 | 0.8094 | 0.4518 |
| 0.6985 | 9.0 | 540 | 0.6731 | 0.8131 | 0.4552 |
| 0.6704 | 10.0 | 600 | 0.6317 | 0.7859 | 0.4177 |
| 0.7633 | 11.0 | 660 | 0.6039 | 0.7867 | 0.4020 |
| 0.644 | 12.0 | 720 | 0.5893 | 0.7256 | 0.4288 |
| 0.5974 | 13.0 | 780 | 0.5308 | 0.7621 | 0.3857 |
| 0.5237 | 14.0 | 840 | 0.4680 | 0.7584 | 0.3572 |
| 0.5087 | 15.0 | 900 | 0.4375 | 0.7476 | 0.3626 |
| 0.4283 | 16.0 | 960 | 0.3604 | 0.5633 | 0.2285 |
| 0.3494 | 17.0 | 1020 | 0.3148 | 0.4832 | 0.1753 |
| 0.7766 | 18.0 | 1080 | 0.2113 | 0.3950 | 0.1374 |
| 0.2481 | 19.0 | 1140 | 0.1601 | 0.3474 | 0.1160 |
| 0.2121 | 20.0 | 1200 | 0.1347 | 0.3220 | 0.1273 |
| 0.1733 | 21.0 | 1260 | 0.0901 | 0.2714 | 0.0981 |
| 0.2682 | 22.0 | 1320 | 0.0754 | 0.2509 | 0.0865 |
| 0.1158 | 23.0 | 1380 | 0.0586 | 0.2368 | 0.0740 |
| 0.1038 | 24.0 | 1440 | 0.0435 | 0.2215 | 0.0788 |
| 0.0807 | 25.0 | 1500 | 0.0309 | 0.1981 | 0.0656 |
| 0.0677 | 26.0 | 1560 | 0.0264 | 0.1977 | 0.0696 |
| 0.0562 | 27.0 | 1620 | 0.0204 | 0.1891 | 0.0698 |
| 0.0775 | 28.0 | 1680 | 0.0160 | 0.1802 | 0.0650 |
| 0.0409 | 29.0 | 1740 | 0.0139 | 0.1813 | 0.0655 |
| 0.0407 | 30.0 | 1800 | 0.0133 | 0.1798 | 0.0658 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "sarulab-speech/hubert-base-jtube", "model-index": [{"name": "hubert_0209_3", "results": []}]} | automatic-speech-recognition | tndklab/hubert_0209_3 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:sarulab-speech/hubert-base-jtube",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:27:22+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us
| hubert\_0209\_3
===============
This model is a fine-tuned version of sarulab-speech/hubert-base-jtube on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0133
* Wer: 0.1798
* Cer: 0.0658
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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115,
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"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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] |
null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-3.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:29:45+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m-lora-1.57M-squad-model2
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-lora-1.57M-squad-model2", "results": []}]} | question-answering | varun-v-rao/opt-350m-lora-1.57M-squad-model2 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:facebook/opt-350m",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:33:24+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-lora-1.57M-squad-model2
This model is a fine-tuned version of facebook/opt-350m on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# opt-350m-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 18\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 18\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
81,
42,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-350m-lora-1.57M-squad-model2\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 18\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: SungjunEom/ppo-PyramidsTraning3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | SungjunEom/ppo-PyramidsTraning3 | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-09T07:35:14+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: SungjunEom/ppo-PyramidsTraning3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-PyramidsTraning3\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-PyramidsTraning3\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
208
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: SungjunEom/ppo-PyramidsTraning3\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:36:36+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4150
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6428 | 1.0 | 625 | 2.4418 |
| 2.5226 | 2.0 | 1250 | 2.4212 |
| 2.4867 | 3.0 | 1875 | 2.4738 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | fill-mask | akuzdeuov/distilbert-base-uncased-finetuned-imdb | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:41:17+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4150
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.33.1
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
] | [
67,
98,
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33
] | [
"passage: TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2-zwjcylk-1
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.9861 | 1.0 | 1004 | 6.8366 |
| 6.2338 | 2.0 | 2008 | 6.4001 |
| 5.8992 | 3.0 | 3012 | 6.2835 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2-zwjcylk-1", "results": []}]} | text-generation | thomaslwang/phi-2-zwjcylk-1 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:43:19+00:00 | [] | [] | TAGS
#transformers #safetensors #phi #text-generation #generated_from_trainer #custom_code #base_model-microsoft/phi-2 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| phi-2-zwjcylk-1
===============
This model is a fine-tuned version of microsoft/phi-2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.2835
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #safetensors #phi #text-generation #generated_from_trainer #custom_code #base_model-microsoft/phi-2 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-4.65bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:44:02+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | transformers |
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Language(s) (NLP):
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | object-detection | sekhharr/detr_finetuned_v3_last_best_checkpoint | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:46:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #detr #object-detection #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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## Evaluation
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- Hardware Type:
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"passage: TAGS\n#transformers #safetensors #detr #object-detection #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:52:45+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert_0209_4
This model is a fine-tuned version of [sarulab-speech/hubert-base-jtube](https://huggingface.co/sarulab-speech/hubert-base-jtube) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3093
- Wer: 0.6048
- Cer: 0.2783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 22
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.1103 | 1.0 | 60 | 3.8972 | 1.0 | 0.9527 |
| 3.0962 | 2.0 | 120 | 2.9877 | 1.0 | 0.9527 |
| 2.3049 | 3.0 | 180 | 2.0233 | 1.0 | 0.9513 |
| 1.3455 | 4.0 | 240 | 1.2537 | 1.0 | 0.6008 |
| 0.9618 | 5.0 | 300 | 0.8425 | 0.8084 | 0.4658 |
| 0.8563 | 6.0 | 360 | 0.7694 | 0.8035 | 0.4904 |
| 0.7323 | 7.0 | 420 | 0.6766 | 0.7979 | 0.4775 |
| 0.683 | 8.0 | 480 | 0.6642 | 0.7987 | 0.4886 |
| 0.6873 | 9.0 | 540 | 0.6185 | 0.7519 | 0.3828 |
| 0.6459 | 10.0 | 600 | 0.5953 | 0.7601 | 0.3863 |
| 0.7102 | 11.0 | 660 | 0.5904 | 0.7668 | 0.3850 |
| 0.6151 | 12.0 | 720 | 0.5186 | 0.6789 | 0.2599 |
| 0.5801 | 13.0 | 780 | 0.4788 | 0.6606 | 0.2563 |
| 0.532 | 14.0 | 840 | 0.4799 | 0.6961 | 0.3013 |
| 0.5186 | 15.0 | 900 | 0.5259 | 0.6740 | 0.2868 |
| 0.5454 | 16.0 | 960 | 0.4824 | 0.6411 | 0.2540 |
| 0.6135 | 17.0 | 1020 | 0.5646 | 0.6669 | 0.2669 |
| 1.1381 | 18.0 | 1080 | 0.4000 | 0.6104 | 0.2281 |
| 0.4341 | 19.0 | 1140 | 0.3624 | 0.6097 | 0.2310 |
| 0.4191 | 20.0 | 1200 | 0.3594 | 0.6370 | 0.2844 |
| 0.3636 | 21.0 | 1260 | 0.3305 | 0.6052 | 0.2668 |
| 0.4295 | 22.0 | 1320 | 0.3093 | 0.6048 | 0.2783 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "sarulab-speech/hubert-base-jtube", "model-index": [{"name": "hubert_0209_4", "results": []}]} | automatic-speech-recognition | tndklab/hubert_0209_4 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:sarulab-speech/hubert-base-jtube",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T07:54:08+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us
| hubert\_0209\_4
===============
This model is a fine-tuned version of sarulab-speech/hubert-base-jtube on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3093
* Wer: 0.6048
* Cer: 0.2783
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 22
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 22",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 22",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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115,
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"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 22### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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] |
null | null | transformers |
# Pasta-Lake-7b 8bpw
Exllama quant of [Test157t/Pasta-Lake-7b](https://huggingface.co/Test157t/Pasta-Lake-7b)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-4bpw-exl2)
## Prompt format: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"]} | text-generation | Kooten/Pasta-Lake-7b-8bpw-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:54:29+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Pasta-Lake-7b 8bpw
Exllama quant of Test157t/Pasta-Lake-7b
## Other quants:
EXL2: 8bpw, 6bpw, 5bpw, 4bpw
## Prompt format: Alpaca
## Contact
Kooten on discord
URL if you would like to support me
| [
"# Pasta-Lake-7b 8bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pasta-Lake-7b 8bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
98,
27,
28,
8,
14
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pasta-Lake-7b 8bpw\nExllama quant of Test157t/Pasta-Lake-7b## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | dmitrybright/Mistral-7B-Instruct-v0.1-4bit | [
"transformers",
"safetensors",
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"text-generation",
"arxiv:1910.09700",
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"region:us"
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"1910.09700"
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#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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APA:
## Glossary [optional]
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | AyushRaj01/llama2_qna_tuned | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
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#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# billsum_4500_t5-base
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1222
- Rouge1: 0.1555
- Rouge2: 0.0612
- Rougel: 0.1268
- Rougelsum: 0.1269
- Gen Len: 18.9943
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2176 | 6.75 | 500 | 2.1222 | 0.1555 | 0.0612 | 0.1268 | 0.1269 | 18.9943 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google-t5/t5-base", "model-index": [{"name": "billsum_4500_t5-base", "results": []}]} | text2text-generation | ryusangwon/billsum_4500_t5-base | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T07:57:51+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| billsum\_4500\_t5-base
======================
This model is a fine-tuned version of google-t5/t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1222
* Rouge1: 0.1555
* Rouge2: 0.0612
* Rougel: 0.1268
* Rougelsum: 0.1269
* Gen Len: 18.9943
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.0.1+cu117
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
77,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
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] |
null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:02:07+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Pooky/awful-falcon-7b-trump | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T08:03:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_qa_finetuned
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 25 | 6.5874 |
| No log | 2.0 | 50 | 6.5470 |
| No log | 3.0 | 75 | 6.6744 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt2_qa_finetuned", "results": []}]} | question-answering | Gigazinie/gpt2_qa_finetuned | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:04:09+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #question-answering #generated_from_trainer #base_model-gpt2 #license-mit #endpoints_compatible #text-generation-inference #region-us
| gpt2\_qa\_finetuned
===================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.6744
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
65,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #question-answering #generated_from_trainer #base_model-gpt2 #license-mit #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Moas_Explicit_OPT_v1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Moas_Explicit_OPT_v1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Moas_Explicit_OPT_v1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Moas_Explicit_OPT_v1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
OPTForCausalLM(
(model): OPTModel(
(decoder): OPTDecoder(
(embed_tokens): Embedding(50272, 2560, padding_idx=1)
(embed_positions): OPTLearnedPositionalEmbedding(2050, 2560)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0-31): 32 x OPTDecoderLayer(
(self_attn): OPTAttention(
(k_proj): Linear(in_features=2560, out_features=2560, bias=True)
(v_proj): Linear(in_features=2560, out_features=2560, bias=True)
(q_proj): Linear(in_features=2560, out_features=2560, bias=True)
(out_proj): Linear(in_features=2560, out_features=2560, bias=True)
)
(activation_fn): ReLU()
(self_attn_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=2560, out_features=10240, bias=True)
(fc2): Linear(in_features=10240, out_features=2560, bias=True)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
)
)
)
(lm_head): Linear(in_features=2560, out_features=50272, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Shishir1807/Moas_Explicit_OPT_v1 | [
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"large language model",
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] | 2024-02-09T08:04:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #opt #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: facebook/opt-2.7b
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
"# Model Card",
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"## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:",
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-to-audio | syedmuhammad/mms-tts-urdu-vits_finetuned-female | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T08:05:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #text-to-audio #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vits #text-to-audio #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #vits #text-to-audio #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Moas_Explicit_PYT_v1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Moas_Explicit_PYT_v1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Moas_Explicit_PYT_v1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Moas_Explicit_PYT_v1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Shishir1807/Moas_Explicit_PYT_v1 | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:05:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neox #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: EleutherAI/pythia-2.8b-deduped
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
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] |
null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-8.0bpw-h8-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:13:15+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
52,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "214.52 +/- 71.33", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | leejeewoo/ppo-LunarLander-v2-1 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T08:13:28+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | transformers |
# Pasta-Lake-7b 6bpw
Exllama quant of [Test157t/Pasta-Lake-7b](https://huggingface.co/Test157t/Pasta-Lake-7b)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-4bpw-exl2)
## Prompt format: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"]} | text-generation | Kooten/Pasta-Lake-7b-6bpw-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:14:10+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Pasta-Lake-7b 6bpw
Exllama quant of Test157t/Pasta-Lake-7b
## Other quants:
EXL2: 8bpw, 6bpw, 5bpw, 4bpw
## Prompt format: Alpaca
## Contact
Kooten on discord
URL if you would like to support me
| [
"# Pasta-Lake-7b 6bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pasta-Lake-7b 6bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
98,
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pasta-Lake-7b 6bpw\nExllama quant of Test157t/Pasta-Lake-7b## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL if you would like to support me"
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null | null | transformers | GLORT2 (GLORT2 Low Rank Transformer Transformer) is a transformer model where every single linear layer is another smaller transformer model. I combined qkv into one operation which means one transformer instead of 3 to save on parameters, I played w using a transformer on the embeddings but it wasnt .. great, it's 768 dim 10 layers w/ 384 dim 1 layer as the replacements for linear layers (besides embed and lm head)
also sorry I just realized theres some residual from where I copied the model code from in my own projects that includes some "expanded lm head size" stuff just ignore that if you're looking at the config and code this isn't a serious project so I don't care too much that it's there
| model | 512-token strided perplexity on a pile test set | tokens |
| --- | --- | --- |
| cerebras 111m | 21.550655364990234 | 2.2b |
| cerebras 256m | 15.203496932983398 | 5.1b |
| cerebras 590m | 12.098200798034668 | 11.something b |
| deduped pythia 70m (95.6M) | 22.393400192260742 | 300b |
| deduped pythia 160m (213M) | 13.933751106262207 | 300b |
| deduped pythia 410m (506M) | 9.61842155456543 | 300b |
| llama w same settings as cerebras 111m (119m) | 13.882301330566406 | 2.2b |
| llama plus w same settings as cerebras 111m and llama 70b embeddings (369m) | 13.565109252929688 | 2.2b |
| **GLORT2 (205m)** | 13.051741600036621 | 2.2b |
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge| 1|none | 25|acc |0.1706|± |0.0110|
| | |none | 25|acc_norm|0.2099|± |0.0119|
|truthfulqa_mc2| 2|none | 0|acc |0.4599|± |0.0154|
|winogrande| 1|none | 5|acc |0.5083|± |0.0141|
|hellaswag| 1|none | 10|acc |0.2728|± |0.0044|
| | |none | 10|acc_norm|0.2815|± |0.0045|
|gsm8k| 2|get-answer| 5|exact_match| 0|± | 0|
### mmlu
mean is 0.26394385964912276 i think
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|-----------------------------------|------:|------|-----:|------|-----:|---|-----:|
|world_religions | 0|none | 5|acc |0.1988|± |0.0306|
|virology | 0|none | 5|acc |0.1928|± |0.0307|
|us_foreign_policy | 0|none | 5|acc |0.2600|± |0.0441|
|sociology | 0|none | 5|acc |0.2438|± |0.0304|
|security_studies | 0|none | 5|acc |0.4000|± |0.0314|
|public_relations | 0|none | 5|acc |0.2273|± |0.0401|
|professional_psychology | 0|none | 5|acc |0.2484|± |0.0175|
|professional_medicine | 0|none | 5|acc |0.4485|± |0.0302|
|professional_law | 0|none | 5|acc |0.2445|± |0.0110|
|professional_accounting | 0|none | 5|acc |0.2482|± |0.0258|
|prehistory | 0|none | 5|acc |0.2562|± |0.0243|
|philosophy | 0|none | 5|acc |0.2186|± |0.0235|
|nutrition | 0|none | 5|acc |0.2941|± |0.0261|
|moral_scenarios | 0|none | 5|acc |0.2503|± |0.0145|
|moral_disputes | 0|none | 5|acc |0.1965|± |0.0214|
|miscellaneous | 0|none | 5|acc |0.2554|± |0.0156|
|medical_genetics | 0|none | 5|acc |0.3000|± |0.0461|
|marketing | 0|none | 5|acc |0.1966|± |0.0260|
|management | 0|none | 5|acc |0.1942|± |0.0392|
|machine_learning | 0|none | 5|acc |0.2321|± |0.0401|
|logical_fallacies | 0|none | 5|acc |0.2331|± |0.0332|
|jurisprudence | 0|none | 5|acc |0.2407|± |0.0413|
|international_law | 0|none | 5|acc |0.3719|± |0.0441|
|human_sexuality | 0|none | 5|acc |0.2137|± |0.0360|
|human_aging | 0|none | 5|acc |0.2646|± |0.0296|
|high_school_world_history | 0|none | 5|acc |0.2489|± |0.0281|
|high_school_us_history | 0|none | 5|acc |0.2304|± |0.0296|
|high_school_statistics | 0|none | 5|acc |0.4722|± |0.0340|
|high_school_psychology | 0|none | 5|acc |0.3083|± |0.0198|
|high_school_physics | 0|none | 5|acc |0.3046|± |0.0376|
|high_school_microeconomics | 0|none | 5|acc |0.3361|± |0.0307|
|high_school_mathematics | 0|none | 5|acc |0.2630|± |0.0268|
|high_school_macroeconomics | 0|none | 5|acc |0.3231|± |0.0237|
|high_school_government_and_politics| 0|none | 5|acc |0.3523|± |0.0345|
|high_school_geography | 0|none | 5|acc |0.3384|± |0.0337|
|high_school_european_history | 0|none | 5|acc |0.2909|± |0.0355|
|high_school_computer_science | 0|none | 5|acc |0.2600|± |0.0441|
|high_school_chemistry | 0|none | 5|acc |0.2709|± |0.0313|
|high_school_biology | 0|none | 5|acc |0.3161|± |0.0265|
|global_facts | 0|none | 5|acc |0.1800|± |0.0386|
|formal_logic | 0|none | 5|acc |0.1667|± |0.0333|
|elementary_mathematics | 0|none | 5|acc |0.2540|± |0.0224|
|electrical_engineering | 0|none | 5|acc |0.3103|± |0.0386|
|econometrics | 0|none | 5|acc |0.2895|± |0.0427|
|conceptual_physics | 0|none | 5|acc |0.2553|± |0.0285|
|computer_security | 0|none | 5|acc |0.1900|± |0.0394|
|college_physics | 0|none | 5|acc |0.3431|± |0.0472|
|college_medicine | 0|none | 5|acc |0.2312|± |0.0321|
|college_mathematics | 0|none | 5|acc |0.1800|± |0.0386|
|college_computer_science | 0|none | 5|acc |0.3000|± |0.0461|
|college_chemistry | 0|none | 5|acc |0.2900|± |0.0456|
|college_biology | 0|none | 5|acc |0.2083|± |0.0340|
|clinical_knowledge | 0|none | 5|acc |0.2038|± |0.0248|
|business_ethics | 0|none | 5|acc |0.2100|± |0.0409|
|astronomy | 0|none | 5|acc |0.1908|± |0.0320|
|anatomy | 0|none | 5|acc |0.2963|± |0.0394|
|abstract_algebra | 0|none | 5|acc |0.2000|± |0.0402| | {"language": ["en"], "datasets": ["EleutherAI/the_pile_deduplicated"]} | text-generation | crumb/GLORT2 | [
"transformers",
"safetensors",
"Transformer",
"text-generation",
"custom_code",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"autotrain_compatible",
"region:us"
] | 2024-02-09T08:15:14+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #Transformer #text-generation #custom_code #en #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #region-us
| GLORT2 (GLORT2 Low Rank Transformer Transformer) is a transformer model where every single linear layer is another smaller transformer model. I combined qkv into one operation which means one transformer instead of 3 to save on parameters, I played w using a transformer on the embeddings but it wasnt .. great, it's 768 dim 10 layers w/ 384 dim 1 layer as the replacements for linear layers (besides embed and lm head)
also sorry I just realized theres some residual from where I copied the model code from in my own projects that includes some "expanded lm head size" stuff just ignore that if you're looking at the config and code this isn't a serious project so I don't care too much that it's there
model: cerebras 111m, 512-token strided perplexity on a pile test set: 21.550655364990234, tokens: 2.2b
model: cerebras 256m, 512-token strided perplexity on a pile test set: 15.203496932983398, tokens: 5.1b
model: cerebras 590m, 512-token strided perplexity on a pile test set: 12.098200798034668, tokens: 11.something b
model: deduped pythia 70m (95.6M), 512-token strided perplexity on a pile test set: 22.393400192260742, tokens: 300b
model: deduped pythia 160m (213M), 512-token strided perplexity on a pile test set: 13.933751106262207, tokens: 300b
model: deduped pythia 410m (506M), 512-token strided perplexity on a pile test set: 9.61842155456543, tokens: 300b
model: llama w same settings as cerebras 111m (119m), 512-token strided perplexity on a pile test set: 13.882301330566406, tokens: 2.2b
model: llama plus w same settings as cerebras 111m and llama 70b embeddings (369m), 512-token strided perplexity on a pile test set: 13.565109252929688, tokens: 2.2b
model: GLORT2 (205m), 512-token strided perplexity on a pile test set: 13.051741600036621, tokens: 2.2b
### mmlu
mean is 0.26394385964912276 i think
| [
"### mmlu\n\n\nmean is 0.26394385964912276 i think"
] | [
"TAGS\n#transformers #safetensors #Transformer #text-generation #custom_code #en #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #region-us \n",
"### mmlu\n\n\nmean is 0.26394385964912276 i think"
] | [
55,
15
] | [
"passage: TAGS\n#transformers #safetensors #Transformer #text-generation #custom_code #en #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #region-us \n### mmlu\n\n\nmean is 0.26394385964912276 i think"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert_0209_5
This model is a fine-tuned version of [sarulab-speech/hubert-base-jtube](https://huggingface.co/sarulab-speech/hubert-base-jtube) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0411
- Wer: 0.2060
- Cer: 0.0643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00045
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.9707 | 1.0 | 60 | 4.2487 | 1.0 | 0.9491 |
| 3.2276 | 2.0 | 120 | 3.0585 | 1.0 | 0.9491 |
| 2.5216 | 3.0 | 180 | 2.2286 | 1.0 | 0.9491 |
| 1.6306 | 4.0 | 240 | 1.3718 | 1.0 | 0.6304 |
| 0.8521 | 5.0 | 300 | 0.7683 | 0.8145 | 0.4821 |
| 0.8236 | 6.0 | 360 | 0.7384 | 0.8122 | 0.4587 |
| 0.7164 | 7.0 | 420 | 0.6450 | 0.7917 | 0.3873 |
| 0.679 | 8.0 | 480 | 0.5892 | 0.7790 | 0.3858 |
| 0.6255 | 9.0 | 540 | 0.5599 | 0.7906 | 0.4153 |
| 0.6178 | 10.0 | 600 | 0.4900 | 0.6693 | 0.2577 |
| 0.6515 | 11.0 | 660 | 0.4840 | 0.6954 | 0.2433 |
| 0.5171 | 12.0 | 720 | 0.4334 | 0.6682 | 0.2387 |
| 0.5019 | 13.0 | 780 | 0.4596 | 0.6626 | 0.2370 |
| 0.4738 | 14.0 | 840 | 0.4103 | 0.6573 | 0.2349 |
| 0.5035 | 15.0 | 900 | 0.4131 | 0.6446 | 0.2255 |
| 0.4597 | 16.0 | 960 | 0.4338 | 0.6424 | 0.2219 |
| 0.5666 | 17.0 | 1020 | 0.4390 | 0.6409 | 0.2210 |
| 1.0526 | 18.0 | 1080 | 0.4544 | 0.6991 | 0.3015 |
| 0.4488 | 19.0 | 1140 | 0.4094 | 0.6480 | 0.2529 |
| 0.4323 | 20.0 | 1200 | 0.3732 | 0.6189 | 0.2155 |
| 0.4194 | 21.0 | 1260 | 0.4308 | 0.6353 | 0.2289 |
| 0.492 | 22.0 | 1320 | 0.3026 | 0.5327 | 0.1796 |
| 0.39 | 23.0 | 1380 | 0.3228 | 0.5401 | 0.2062 |
| 0.3461 | 24.0 | 1440 | 0.3162 | 0.5315 | 0.1937 |
| 0.2925 | 25.0 | 1500 | 0.2446 | 0.4763 | 0.1634 |
| 0.2935 | 26.0 | 1560 | 0.2233 | 0.4408 | 0.1470 |
| 0.274 | 27.0 | 1620 | 0.2175 | 0.4296 | 0.1438 |
| 0.2742 | 28.0 | 1680 | 0.1932 | 0.3934 | 0.1327 |
| 0.2146 | 29.0 | 1740 | 0.1798 | 0.3841 | 0.1290 |
| 0.2245 | 30.0 | 1800 | 0.1589 | 0.3673 | 0.1405 |
| 0.2154 | 31.0 | 1860 | 0.1360 | 0.3214 | 0.1147 |
| 0.2098 | 32.0 | 1920 | 0.1189 | 0.3024 | 0.1020 |
| 0.1788 | 33.0 | 1980 | 0.1169 | 0.3009 | 0.0980 |
| 0.1465 | 34.0 | 2040 | 0.0871 | 0.2639 | 0.0906 |
| 0.1816 | 35.0 | 2100 | 0.0767 | 0.2456 | 0.0793 |
| 0.1178 | 36.0 | 2160 | 0.0746 | 0.2475 | 0.0806 |
| 0.0985 | 37.0 | 2220 | 0.0525 | 0.2240 | 0.0715 |
| 0.0979 | 38.0 | 2280 | 0.0483 | 0.2206 | 0.0710 |
| 0.1018 | 39.0 | 2340 | 0.0428 | 0.2090 | 0.0650 |
| 0.1388 | 40.0 | 2400 | 0.0411 | 0.2060 | 0.0643 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "sarulab-speech/hubert-base-jtube", "model-index": [{"name": "hubert_0209_5", "results": []}]} | automatic-speech-recognition | tndklab/hubert_0209_5 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:sarulab-speech/hubert-base-jtube",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T08:15:23+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us
| hubert\_0209\_5
===============
This model is a fine-tuned version of sarulab-speech/hubert-base-jtube on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0411
* Wer: 0.2060
* Cer: 0.0643
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.00045
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 40
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
66,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-sarulab-speech/hubert-base-jtube #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 40### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | diffusers | # Dove_Cameron_Disney_era
<Gallery />
## Model description
Here's the 300 epochs RVC voice model of Dove Cameron (Disney era)
## Trigger words
You should use `Please spare me` to trigger the image generation.
## Download model
[Download](/LegoClipStars/Dove_Cameron_Disney_era/tree/main) them in the Files & versions tab.
| {"license": "cc-by-4.0", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "NEFT", "parameters": {"negative_prompt": "American singer"}, "output": {"url": "images/dww-ss3.jpg"}}], "base_model": "cagliostrolab/animagine-xl-3.0", "instance_prompt": "Please spare me"} | text-to-image | LegoClipStars/Dove_Cameron_Disney_era | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"license:cc-by-4.0",
"region:us"
] | 2024-02-09T08:18:32+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-cc-by-4.0 #region-us
| # Dove_Cameron_Disney_era
<Gallery />
## Model description
Here's the 300 epochs RVC voice model of Dove Cameron (Disney era)
## Trigger words
You should use 'Please spare me' to trigger the image generation.
## Download model
Download them in the Files & versions tab.
| [
"# Dove_Cameron_Disney_era\n\n<Gallery />",
"## Model description \n\nHere's the 300 epochs RVC voice model of Dove Cameron (Disney era)",
"## Trigger words\n\nYou should use 'Please spare me' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-cc-by-4.0 #region-us \n",
"# Dove_Cameron_Disney_era\n\n<Gallery />",
"## Model description \n\nHere's the 300 epochs RVC voice model of Dove Cameron (Disney era)",
"## Trigger words\n\nYou should use 'Please spare me' to trigger the image generation.",
"## Download model\n\n\nDownload them in the Files & versions tab."
] | [
60,
15,
24,
18,
14
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.0 #license-cc-by-4.0 #region-us \n# Dove_Cameron_Disney_era\n\n<Gallery />## Model description \n\nHere's the 300 epochs RVC voice model of Dove Cameron (Disney era)## Trigger words\n\nYou should use 'Please spare me' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab."
] | [
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-bn-adapter-3.17M-squad-model3
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 19
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "bert-large-cased", "model-index": [{"name": "bert-large-cased-bn-adapter-3.17M-squad-model3", "results": []}]} | null | varun-v-rao/bert-large-cased-bn-adapter-3.17M-squad-model3 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:bert-large-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-09T08:22:03+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us
|
# bert-large-cased-bn-adapter-3.17M-squad-model3
This model is a fine-tuned version of bert-large-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 19
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# bert-large-cased-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 19\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us \n",
"# bert-large-cased-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 19\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
50,
48,
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3,
90,
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33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-large-cased #license-apache-2.0 #region-us \n# bert-large-cased-bn-adapter-3.17M-squad-model3\n\nThis model is a fine-tuned version of bert-large-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 19\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | diffusers |
# Textual inversion text2image fine-tuning - seb1234/textual_inversion_car
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | text-to-image | seb1234/textual_inversion_car | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-09T08:23:37+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Textual inversion text2image fine-tuning - seb1234/textual_inversion_car
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| [
"# Textual inversion text2image fine-tuning - seb1234/textual_inversion_car\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - seb1234/textual_inversion_car\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
101,
60
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# Textual inversion text2image fine-tuning - seb1234/textual_inversion_car\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | regisss/test_model_2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
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"1910.09700"
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#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2.dev0 | {"library_name": "peft", "base_model": "intelsense/IntelsenseMistral1stPhase"} | null | RadAlienware/mis_mod_bn_2ndphase1 | [
"peft",
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|
# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
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- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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APA:
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null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "Minecraft bucket assets", "inference": true} | text-to-image | johnBenson00/Test | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-09T08:26:00+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Tr - tgrhn
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3562
- Wer: 159.3723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.078 | 1.1 | 1000 | 0.2926 | 211.8713 |
| 0.0243 | 3.09 | 2000 | 0.3126 | 196.7295 |
| 0.0064 | 5.09 | 3000 | 0.3389 | 219.2120 |
| 0.0026 | 7.08 | 4000 | 0.3515 | 112.3528 |
| 0.0027 | 9.08 | 5000 | 0.3562 | 159.3723 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"language": ["tr"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Tr - tgrhn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.0", "type": "mozilla-foundation/common_voice_16_0", "config": "tr", "split": "test", "args": "tr"}, "metrics": [{"type": "wer", "value": 159.3722540508242, "name": "Wer"}]}]}]} | automatic-speech-recognition | tgrhn/whisper-small-tr | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"tr",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-09T08:30:35+00:00 | [] | [
"tr"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Small Tr - tgrhn
========================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3562
* Wer: 159.3723
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.1
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
100,
130,
4,
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_16_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
# Kaggle ASL Signs
This directory includes the winning entry for the
[ASL Signs](https://www.kaggle.com/competitions/asl-signs) Kaggle competition.
Files are from
- the model from the top solution: https://www.kaggle.com/code/hoyso48/1st-place-solution-inference
- the [ASL vocabulary](https://www.kaggle.com/competitions/asl-signs/data?select=sign_to_prediction_index_map.json) of the training data
| {"license": "mit"} | null | sign/kaggle-asl-signs-1st-place | [
"tflite",
"license:mit",
"region:us"
] | 2024-02-09T08:36:45+00:00 | [] | [] | TAGS
#tflite #license-mit #region-us
|
# Kaggle ASL Signs
This directory includes the winning entry for the
ASL Signs Kaggle competition.
Files are from
- the model from the top solution: URL
- the ASL vocabulary of the training data
| [
"# Kaggle ASL Signs\n\nThis directory includes the winning entry for the\nASL Signs Kaggle competition.\n\nFiles are from \n- the model from the top solution: URL\n- the ASL vocabulary of the training data"
] | [
"TAGS\n#tflite #license-mit #region-us \n",
"# Kaggle ASL Signs\n\nThis directory includes the winning entry for the\nASL Signs Kaggle competition.\n\nFiles are from \n- the model from the top solution: URL\n- the ASL vocabulary of the training data"
] | [
15,
48
] | [
"passage: TAGS\n#tflite #license-mit #region-us \n# Kaggle ASL Signs\n\nThis directory includes the winning entry for the\nASL Signs Kaggle competition.\n\nFiles are from \n- the model from the top solution: URL\n- the ASL vocabulary of the training data"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Basha738/llama2-supervised-ft-5epochs-411 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-09T08:38:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
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### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
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"## Model Details",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
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"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m-lora-1.57M-squad-model3
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-lora-1.57M-squad-model3", "results": []}]} | question-answering | varun-v-rao/opt-350m-lora-1.57M-squad-model3 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"question-answering",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:facebook/opt-350m",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:39:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us
|
# opt-350m-lora-1.57M-squad-model3
This model is a fine-tuned version of facebook/opt-350m on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# opt-350m-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n",
"# opt-350m-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
81,
42,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-350m #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-350m-lora-1.57M-squad-model3\n\nThis model is a fine-tuned version of facebook/opt-350m on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | sentence-transformers |
# Phi2 Model Trained for retrieval task using MSMarco Dataset
### Trained for 1 epoch using the tevatron library
#### Ongoing work | {"language": ["en"], "license": "mit", "library_name": "sentence-transformers", "tags": ["mteb"], "datasets": ["Tevatron/msmarco-passage-corpus", "Tevatron/msmarco-passage"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "ret-phi2-v0", "results": [{"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "arguana", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 24.609}, {"type": "map_at_10", "value": 39.404}, {"type": "map_at_100", "value": 40.421}, {"type": "map_at_1000", "value": 40.437}, {"type": "map_at_3", "value": 34.258}, {"type": "map_at_5", "value": 37.078}, {"type": "mrr_at_1", "value": 24.822}, {"type": "mrr_at_10", "value": 39.48}, {"type": "mrr_at_100", "value": 40.498}, {"type": "mrr_at_1000", "value": 40.513}, {"type": "mrr_at_3", "value": 34.436}, {"type": "mrr_at_5", "value": 37.156}, {"type": "ndcg_at_1", "value": 24.609}, {"type": "ndcg_at_10", "value": 48.274}, {"type": "ndcg_at_100", "value": 52.654}, {"type": "ndcg_at_1000", "value": 53.037}, {"type": "ndcg_at_3", "value": 37.558}, {"type": "ndcg_at_5", "value": 42.678}, {"type": "precision_at_1", "value": 24.609}, {"type": "precision_at_10", "value": 7.688000000000001}, {"type": "precision_at_100", "value": 0.962}, {"type": "precision_at_1000", "value": 0.099}, {"type": "precision_at_3", "value": 15.717999999999998}, {"type": "precision_at_5", "value": 11.935}, {"type": "recall_at_1", "value": 24.609}, {"type": "recall_at_10", "value": 76.885}, {"type": "recall_at_100", "value": 96.15899999999999}, {"type": "recall_at_1000", "value": 99.14699999999999}, {"type": "recall_at_3", "value": 47.155}, {"type": "recall_at_5", "value": 59.673}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB CQADupstackAndroidRetrieval", "type": "BeIR/cqadupstack", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 27.916}, {"type": "map_at_10", 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"value": 4.603}, {"type": "map_at_5", "value": 6.517}, {"type": "mrr_at_1", "value": 26.531}, {"type": "mrr_at_10", "value": 43.608000000000004}, {"type": "mrr_at_100", "value": 44.175}, {"type": "mrr_at_1000", "value": 44.190000000000005}, {"type": "mrr_at_3", "value": 37.755}, {"type": "mrr_at_5", "value": 41.531}, {"type": "ndcg_at_1", "value": 25.509999999999998}, {"type": "ndcg_at_10", "value": 25.663999999999998}, {"type": "ndcg_at_100", "value": 37.362}, {"type": "ndcg_at_1000", "value": 48.817}, {"type": "ndcg_at_3", "value": 23.223}, {"type": "ndcg_at_5", "value": 24.403}, {"type": "precision_at_1", "value": 26.531}, {"type": "precision_at_10", "value": 24.694}, {"type": "precision_at_100", "value": 7.776}, {"type": "precision_at_1000", "value": 1.541}, {"type": "precision_at_3", "value": 23.810000000000002}, {"type": "precision_at_5", "value": 25.306}, {"type": "recall_at_1", "value": 2.4619999999999997}, {"type": "recall_at_10", "value": 17.712}, {"type": "recall_at_100", "value": 48.232}, {"type": "recall_at_1000", "value": 83.348}, {"type": "recall_at_3", "value": 5.763}, {"type": "recall_at_5", "value": 9.577}]}]}]} | sentence-similarity | tanmaylaud/ret-phi2-v0 | [
"sentence-transformers",
"safetensors",
"phi",
"mteb",
"sentence-similarity",
"custom_code",
"en",
"dataset:Tevatron/msmarco-passage-corpus",
"dataset:Tevatron/msmarco-passage",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-09T08:44:10+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #safetensors #phi #mteb #sentence-similarity #custom_code #en #dataset-Tevatron/msmarco-passage-corpus #dataset-Tevatron/msmarco-passage #license-mit #model-index #endpoints_compatible #region-us
|
# Phi2 Model Trained for retrieval task using MSMarco Dataset
### Trained for 1 epoch using the tevatron library
#### Ongoing work | [
"# Phi2 Model Trained for retrieval task using MSMarco Dataset",
"### Trained for 1 epoch using the tevatron library",
"#### Ongoing work"
] | [
"TAGS\n#sentence-transformers #safetensors #phi #mteb #sentence-similarity #custom_code #en #dataset-Tevatron/msmarco-passage-corpus #dataset-Tevatron/msmarco-passage #license-mit #model-index #endpoints_compatible #region-us \n",
"# Phi2 Model Trained for retrieval task using MSMarco Dataset",
"### Trained for 1 epoch using the tevatron library",
"#### Ongoing work"
] | [
85,
17,
15,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #phi #mteb #sentence-similarity #custom_code #en #dataset-Tevatron/msmarco-passage-corpus #dataset-Tevatron/msmarco-passage #license-mit #model-index #endpoints_compatible #region-us \n# Phi2 Model Trained for retrieval task using MSMarco Dataset### Trained for 1 epoch using the tevatron library#### Ongoing work"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | bhuvanmdev/falcon-7b-finetunned-orderbot | [
"transformers",
"safetensors",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | sanmaro6803/llama2-ko-7b-ds-qlora-sft-constlr1e-4-merged | [
"transformers",
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"llama",
"text-generation",
"arxiv:1910.09700",
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#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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### Compute Infrastructure
#### Hardware
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
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## Model Card Contact
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null | null | transformers |
# TasnimSum-MT5 Summary
This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on [TasnimNews Dataset] (https://www.kaggle.com/datasets/amirpourmand/tasnimdataset) using the pretrained model [pn-summary-mt5-small](https://huggingface.co/HooshvareLab/pn-summary-mt5-small).
## Usage
```python
from transformers import AutoModelForSeq2SeqLM, MT5Tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('PardisSzah/TasnimSum-MT5')
tokenizer = MT5Tokenizer.from_pretrained("PardisSzah/TasnimSum-MT5")
text_to_summarize = """
احسان ارکانی نماینده مردم نیشابور در مجلس شورای اسلامی در گفتوگو با خبرنگار پارلمانی ، از ارائه طرح 2 فوریتی شفافیت قوای سهگانه
دستگاههای اجرایی و نهادهای عمومی غیردولتی به هیئت رئیسه مجلس خبر داد. نماینده نیشابور
در مجلس با بیان اینکه بیش از 200 تن از نمایندگان طرح شفافیت قوای سهگانه را امضا کردهاند،
گفت: با توجه به اینکه، این طرح با 2 فوریت تقدیم هیئت رئیسه شد طبق آییننامه داخلی مجلس باید هفته آینده در دستورکار مجلس قرار گیرد.
متن کامل طرح 2 فوریتی شفافیت قوای سهگانه دستگاههای اجرایی و نهادهای عمومی غیردولتی به شرح ذیل است:
مقدمه (دلایل توجیهی): نظر به اینکه نظام مقدس جمهوری اسلامی ایران به تعبیر امامین انقلاب اسلامی متکی بر آرای عمومی است و لازمه این موضوع
اعتماد عموم مردم به نمایندگان و مدیران و مسئولان اعم از انتخابی و انتصابی میباشد،
برای ارتقای سرمایه اجتماعی در جمهوری اسلامی ایران و تقویت باور عمومی که هیچ چیز از مردم پنهان نبوده
و تمامی مسئولان و دولتمردان و مدیران و نمایندگان در جمهوری اسلامی ایران به این امر باور دارند که
باید اطلاعات مربوط به حوزه فعالیت دستگاههای متبوع خود را به همراه مذاکرات و تصمیمات متخذه که قاعدتا بر حقوق تمامی مردم یا بخش عمدهای از جامعه اثرگذار است
در اختیار عموم مردم قرار دهند و امکان داوری را برای مردم فراهم سازند و اینکه تاکنون تلاشهای صورت گرفته در این راستا نتوانسته موفقتآمیز بوده
و بعضا به دلایل مختلف منجر به مخالفتهایی شده و بر همین اساس هم به نتیجه منتهی نشده است لذا (طرح شفافیت قوای سهگانه و دستگاههای اجرایی و سایر نهادها)
به منظور رفع خلاء قانونی و تحولی در نظام حکمرانی کشور تهیه و به شرح زیر برای امضای نمایندگان محترم و تصویب در صحن علنی مجلس تقدیم میگردد.
عنوان طرح: طرح شفافیت قوای سه گانه و دستگاههای اجرایی و سایر نهادها موضوع طرح
: الف) دامنه شمول قانون ماده 1- دستگاهها و نهادها و سازمان ها و موسسات مشمول این قانون
عبارتند از: 1-1- کلیه دستگاههای اجرایی موضوع آمده 5 قانون مدیریت خدمات کشوری؛ از جمله هیئت وزیران و کلیه کمیسیونها و دستگاههای متشکله مرکب از وزراء
یا مدیران دستگاههای دولتی و کلیه وزارتخانهها، سازمانها، موسسات، شرکتهای دولتی، موسسات انتفاعی وابسته به دولت، بانکها و موسسات اعتباری دولتی،
و همچنین دستگاههایی که شمول قانون بر آنها مستلزم ذکر یا تصریح نام است
، اعم از اینکه قانون خاص خود را داشته و یا از قوانین و مقررات عام تبعیت نمایند، از جمله شرکت ملی نفت ایران، شرکت ملی گاز ایران،
شرکت ملی صنایع پتروشیمی ایران، سازمان گسترش و نوسازی صنایع ایران، سازمان بنادر و کشتیرانی جمهوری اسلامی ایران،
سازمان صدا و سیمای جمهوری اسلامی ایران و تمام موسسات و شرکتهای تابع یا وابسته به آنها، دانشگاهها و موسسات آموزشی و
پژوهشی و موسسات و نهادهای عمومی غیر دولتی موضوع ماده 3 قانون مدیریت خدمات کشوری - مصوب 1386 - از جمله شهرداریها، کمیته امداد امام خمینی، هلال احمر جمهوری اسلامی ایران
و تمامی سازمانها، موسسات و شرکتهای تابع یا وابسته به آنها و شوراهای اسلامی شهر و روستا، بخش، شهرستان، استان و شورای عالی استانها. 1-2- قوه مقننه شامل مجلس شورای اسلامی،
دیوان محاسبات کشور و تمامی سازمانها، موسسات و شرکتهای تابع یا وابسته به آنها. 1-3- قوه قضائیه شامل دادسراها و دادگاههای دادگستری اعم از دادگاههای عمومی و ویژه،
سازمان بازرسی کل کشور، دیوان عدالت اداری، سازمان قضایی نیروهای مسلح و تمامی سازمانها، موسسات و شرکتهای تابع یا وابسته به آنها و همچنین شورای عالی حل اختلاف.
1-4- مجمع تشخیص مصلحت نظام، شورای عالی انقلاب فرهنگی، شورای عالی فضای مجازی و کلیه شوراهای عالی که به موجب قانون مصوب مجلس تشکیل شدهاند.
1-5- کلیه موسسات عهدهدار خدمات عمومی از جمله شامل کانونهای وکلای دادگستری، کانون کارشناسان رسمی دادگستری،
سازمان نظام پزشکی جمهوری اسلامی ایران، سازمانهای نظام مهندسی، اتاقهای بازرگانی، صنایع، معادن و کشاورزی ایران، اصناف و تعاون، دانشگاه آزاد اسلامی،
دانشگاهها و موسسات آموزش عالی، مدارس و موسسات اعتباری و شرکتهای بیمه غیر دولتی. ب) نحوه اقدام ماده 2- مشمولین این قانون موظفند داده ها
و اطلاعات عمومی و تصمیمات متخذه سازمان یا شورای متبوع خود را به موجب قوانین و مقررات از طریق سامانهةای مربوط به خود منتشر و اطلاعات رسانی نمایند
، به طوری که عدم بارگزاری هر کدام از اطلاعات، به معنای محرمانه بودن اطلاعات مربوط به حساب آید.
ماده 3- رئیس مجلس شورای اسلامی و رئیس قوه قضائیه موظفند مصادیق داده و اطلاعات عمومی در هر کدام
از نهادها و دستگاههای متبوع خود را علاوه بر مواردی که به عنوان اطلاعات عمومی در قانون انتشار و دسترسی آزاد به اطلاعات مشخص شده است معین کرده
و پس از 4 ماه از تصویب این قانون جهت دسترسی عمومی به این اطلاعات به کمیسیون انتشار و دسترسی آزاد به
اطلاعات ابلاغ نمایند. ماده 5: تمامی نهدهای شورایی کشور مشتمل بر مجلس شورای اسلامی، مجمع تشخیص مصلحت نظام، شوراهای شهر و روستاف شوراهای عالی کشور موظفند
مشروح مذاکرات خود را اعم از صحن و کمیسیونهای تابع آنها و آرای ماخوذه از اعضا را بلافاصله در پایگاه اطلاعرسانی خود منتشر نمایند.
آییننامه محرمانگی و غیرعلنی بودن جلسات هر کدام از نهادها و شوراها پس از سه ماه از تصویب این قانون باید تعیین و پس از تصویب شورای امنیت ملی به اطلاع عموم رسانده شود
. ماده 6: وزارت ارتباطات و فناوری اطلاعات موظف است ظرف سه ماه از تاریخ لازمالاجرا شدن این قانون،
با همکاری سازمان اداری و استخدامی کشور و دستگاههای ذیربط، ضوابط ایجاد، نگهداری و
اشتراکگذاری اطلاعات شامل طراحی فرمها و قالبهای مورد قبول اسناد را برای تمامی دستگاهها و نهادهای مشمول این قانون، به گونهای تدوین کند
و پس از تصویب شورای اجرای فناوری اطلاعات با تایید شورای امنیت ملی، جهت اجرا ابلاغ نماید که اطلاعات منتشر یا به اشتراک گذاشته شده
توسط موسسات مشمول بدون وابستگی به فناوریهای خاص توسط نرمافزارهای متنوع قابل استفاده و پردازش باشند.
همچنین، ضوابط ایجاد، اداره و بهروزرسانی پایگاه های اطلاعرسانی موسسات مشمول را از جهت محتوا، سهولت و نحوه دسترسی کاربران،
قابل اعتماد بودن و قابل فهم بودن برای عموم مردم، توسط وزارت ارتباطات و فناوری اطلاعات تهیه شده، پس از تصویب شورای اجرایی فناوری اطلاعات جهت اجرا ابلاغ میگردد.
ماده 7: عدم اجرا یا اجرای ناقص تکالیف مقرر در این قانون با انتشار اطلاعات مغایر مفاد
این قانون تخلف محسوب و متخلف یا متخلفان حسب مورد بر اساس رای هیئتهای رسیدگی به تخلفات اداری و با توجه به اهمیت موضوع به یکی از مجازاتهای اداری مقرر
در بندهای (ج) تا (ی) ماده (9) قانون رسیدگی به تخلفات اداری - مصوب 1372 - یا مجازات متناسب مطابق قوانین مربوط محکوم میشوند. انتهای پیام/
"""
inputs = tokenizer(text_to_summarize, truncation=True, max_length=256, padding='max_length', return_tensors="pt")
# Now you can generate the summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=64, early_stopping=True)
# Decode the summary
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)
# output: نماینده نیشابور در مجلس از ارائه طرح ۲ فوریتی شفافیت قوای سه گانه دستگاه های اجرایی و نهادهای عمومی غیردولتی به هیئت رئیسه مجلس خبر داد.
```
| {"language": "fa", "license": "mit", "pipeline_tag": "summarization", "inference": false} | summarization | PardisSzah/TasnimSum-MT5 | [
"transformers",
"safetensors",
"mt5",
"text-classification",
"summarization",
"fa",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:50:51+00:00 | [] | [
"fa"
] | TAGS
#transformers #safetensors #mt5 #text-classification #summarization #fa #license-mit #autotrain_compatible #text-generation-inference #region-us
|
# TasnimSum-MT5 Summary
This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on [TasnimNews Dataset] (URL using the pretrained model pn-summary-mt5-small.
## Usage
| [
"# TasnimSum-MT5 Summary\n\nThis model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on [TasnimNews Dataset] (URL using the pretrained model pn-summary-mt5-small.",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mt5 #text-classification #summarization #fa #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# TasnimSum-MT5 Summary\n\nThis model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on [TasnimNews Dataset] (URL using the pretrained model pn-summary-mt5-small.",
"## Usage"
] | [
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"passage: TAGS\n#transformers #safetensors #mt5 #text-classification #summarization #fa #license-mit #autotrain_compatible #text-generation-inference #region-us \n# TasnimSum-MT5 Summary\n\nThis model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on [TasnimNews Dataset] (URL using the pretrained model pn-summary-mt5-small.## Usage"
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