|
|
--- |
|
|
license: llama2 |
|
|
library_name: peft |
|
|
tags: |
|
|
- llama-factory |
|
|
- lora |
|
|
- generated_from_trainer |
|
|
base_model: Yhyu13/LMCocktail-10.7B-v1 |
|
|
model-index: |
|
|
- name: LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora |
|
|
|
|
|
This model is a fine-tuned version of [Yhyu13/LMCocktail-10.7B-v1](https://huggingface.co/Yhyu13/LMCocktail-10.7B-v1) on the https://huggingface.co/datasets/Yhyu13/glaive-function-calling-v2-llama-factory-convert dataset, but with a subset of only the first 2000 data entries. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.2787 |
|
|
|
|
|
Training script is availbale at ./scripts/ |
|
|
|
|
|
## 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: 1 |
|
|
- eval_batch_size: 1 |
|
|
- seed: 42 |
|
|
- distributed_type: multi-GPU |
|
|
- num_devices: 2 |
|
|
- gradient_accumulation_steps: 4 |
|
|
- total_train_batch_size: 8 |
|
|
- total_eval_batch_size: 2 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: cosine |
|
|
- num_epochs: 1.0 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|
|:-------------:|:-----:|:----:|:---------------:| |
|
|
| 0.2696 | 1.0 | 747 | 0.2787 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.7.1 |
|
|
- Transformers 4.36.2 |
|
|
- Pytorch 2.1.2+cu121 |
|
|
- Datasets 2.16.1 |
|
|
- Tokenizers 0.15.0 |
