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README.md

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 ---
 base_model: Qwen/Qwen2.5-32B-Instruct
 library_name: peft
+license: apache-2.0
+datasets:
+- GAIR/LIMO
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-#### Summary
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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-### Framework versions
-
-- PEFT 0.12.0
+# qwen2.5-32b-instruct-limo-lora-adapter
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) model. The fine-tuning was performed using Low-Rank Adaptation (LoRA) on the [LIMO dataset](https://huggingface.co/datasets/GAIR/LIMO) to enhance the model's reasoning capabilities, based on the work in the paper: [LIMO: Less is More for Reasoning](https://arxiv.org/pdf/2502.03387).
+
+## Model description
+
+- **Base Model**: [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
+- **Fine-Tuning Dataset**: [GAIR/LIMO](https://huggingface.co/datasets/GAIR/LIMO)
+- **Fine-Tuning Method**: Low-Rank Adaptation (LoRA)
+- **Library Used**: [peft](https://github.com/huggingface/peft)
+- **License**: [Apache 2.0](LICENSE)
+
+## Usage
+
+To utilize this model for text generation tasks, follow the steps below:
+
+### Installation
+
+Ensure you have the necessary libraries installed:
+
+```bash
+pip install torch transformers peft
+```
+
+### Generating Text
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load the base model
+base_model_name = "Qwen/Qwen2.5-32B-Instruct"
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype="auto", device_map="auto")
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+# Load the LoRA adapter
+adapter_path = "t83714/qwen2.5-32b-instruct-limo-lora-adapter"
+model = PeftModel.from_pretrained(base_model, adapter_path)
+prompt = "How much is (2+5)x5/7"
+# Tokenize the input
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+# Generate the output
+output = model.generate(**inputs, max_length=8000)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Merge the adapter and export merged model
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-32B-Instruct")
+# Load the LoRA adapter
+adapter_path = "t83714/qwen2.5-32b-instruct-limo-lora-adapter"
+model = PeftModel.from_pretrained(base_model, adapter_path)
+merged_model = model.merge_and_unload()
+merged_model.save_pretrained("./merged-model/")
+```
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-06
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- generation_max_length: 16384
+- lr_scheduler_type: cosine
+- num_epochs: 15
+- lora rank: 8
+- lora target layers:
+  - v_proj
+  - o_proj
+  - q_proj
+  - k_proj
+
+
+
+## Eval Result
+
+[Math 500](https://github.com/GAIR-NLP/LIMO/blob/main/eval/data/math/test.jsonl) pass@1: 85%
+
+
+## Acknowledgment
+This model is trained based on the work of [Ye et al. (2025)](https://arxiv.org/abs/2502.03387). If you use this model, please also consider citing their paper:
+```bibtex
+@misc{ye2025limoreasoning,
+      title={LIMO: Less is More for Reasoning}, 
+      author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
+      year={2025},
+      eprint={2502.03387},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.03387}, 
+}
+```

eval/math/test_qwen-instruct_t0.0_k1_s0_e500_gen_round0.pkl

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