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--- |
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base_model: unsloth/Qwen2.5-0.5B-Instruct |
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library_name: peft |
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license: mit |
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datasets: |
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- HoangHa/pensez-grpo |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- math |
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- trl |
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- unsloth |
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- grpo |
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- transformers |
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--- |
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# Model Card for Math-RL |
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## Model Details |
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This model is a fine-tuned version of Qwen2.5-0.5B-Instruct, optimized with Group Relative Policy Optimization (GRPO) on a curated math dataset of 700 problems. |
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The fine-tuning process aims to enhance the model’s step-by-step reasoning ability in mathematical problem solving, improving its performance on structured reasoning tasks. |
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### Model Description |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** Qwen2.5-0.5B-Instruct |
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- **Fine-tuning Method**: GRPO with LoRa |
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- **Domain**: Mathematics (problem-solving, reasoning) |
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- **Dataset Size**: ~700 examples |
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## Uses |
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### Direct Use |
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The model is intended for: |
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- Educational purposes: assisting students with math problems |
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- Research on small-scale RLHF-style fine-tuning (GRPO) |
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- Experiments in reasoning with small instruction-tuned models |
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- Serving as a lightweight math reasoning assistant in constrained environments |
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## Bias, Risks, and Limitations |
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- Small Dataset: Fine-tuned only on 700 math problems, so generalization is limited. |
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- Reasoning Errors: May produce incorrect or hallucinated answers. Always verify results. |
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- Not a Math Oracle: Should not be used in high-stakes scenarios (e.g., exams, grading, critical calculations). |
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- Limited Scope: Performance is strongest on problems similar to the fine-tuning dataset; outside domains may degrade. |
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- Language: While the base model supports multiple languages, math-specific fine-tuning was primarily English-based. |
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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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```python |
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from huggingface_hub import login |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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login(token="") |
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tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct",) |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"unsloth/Qwen2.5-0.5B-Instruct", |
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device_map={"": 0}, token="" |
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) |
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model = PeftModel.from_pretrained(base_model,"Rustamshry/Math-RL") |
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question = """ |
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Translate the graph of the function $y=\sin 2x$ along the $x$-axis to the left by $\dfrac{\pi }{6}$ units, and stretch the ordinate to twice its original length (the abscissa remains unchanged) to obtain the graph of the function $y=f(x)$. If the minimum value of the function $y=f(x)+a$ on the interval $\left[ 0,\dfrac{\pi }{2} \right]$ is $\sqrt{3}$, then $a=\boxed{\_\_\_\_\_}$. |
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""" |
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system = """ |
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Respond in the following format: |
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<reasoning> |
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... |
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</reasoning> |
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<answer> |
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... |
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</answer> |
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""" |
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messages = [ |
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{"role" : "system", "content" : system}, |
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{"role" : "user", "content" : question} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize = False, |
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) |
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from transformers import TextStreamer |
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_ = model.generate( |
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**tokenizer(text, return_tensors = "pt").to("cuda"), |
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max_new_tokens = 2048, |
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streamer = TextStreamer(tokenizer, skip_prompt = True), |
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) |
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``` |
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### Framework versions |
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- PEFT 0.15.2 |
