emre
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Qwen-0.5B-GRPO

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-- grpo
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 ---
 library_name: transformers
 tags:
+  - trl
+  - grpo
+  - qwen
+  - gsm8k
 ---
+# Qwen-0.5B-GRPO: A Fine-Tuned Math Reasoner
+This model is a fine-tuned version of the Qwen 0.5B model (based on [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)) using GRPO (Generative Reward Policy Optimization). It has been trained on the GSM8K math dataset to improve its ability to generate step-by-step reasoning for math problems, following a structured output format with explicit `<reasoning>` and `<answer>` sections.
 ## Model Details
 ### Model Description
+Qwen-0.5B-GRPO is designed to serve as a lightweight math reasoning assistant. By fine-tuning with reinforcement learning using GRPO, the model learns to produce responses that include both intermediate reasoning and final answers. Key adaptations include:
+- **Base Model:** Qwen/Qwen2.5-0.5B-Instruct
+- **Fine-Tuning Method:** GRPO (reinforcement learning with custom reward functions)
+- **Dataset:** GSM8K – a collection of challenging grade-school math problems
+- **Generation Engine:** Utilizes vLLM for faster inference on a single GPU setup
+- **Precision:** BF16 training for efficiency on Colab GPUs
+- **Developed by:** [Your Name or Organization]
+- **License:** Please refer to the license of the base model on its Hugging Face Hub page
+### Model Sources
+- **Repository (this model):** [https://huggingface.co/emre/Qwen-0.5B-GRPO](https://huggingface.co/emre/Qwen-0.5B-GRPO)
+- **Base Model Repository:** [https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **Dataset:** [https://huggingface.co/datasets/openai/gsm8k](https://huggingface.co/datasets/openai/gsm8k)
 ## Uses
+### Intended Use
+This model is intended for educational and research purposes, particularly to demonstrate and support math problem solving with clear, step-by-step reasoning. It is well-suited for:
+- Generating structured explanations for math problems.
+- Serving as a lightweight assistant in educational applications focused on math reasoning.
 ### Out-of-Scope Use
+- **High-Stakes Decision Making:** This model is not designed for critical decision making.
+- **Non-Math Domains:** Its performance is tailored to math problems; performance on other domains may be limited.
+- **Over-Reliance on Automated Reasoning:** The reward functions used during fine-tuning (e.g., exact string matching) may not capture all nuances, so human oversight is recommended.
 ## Bias, Risks, and Limitations
+- **Model Size:** With only 0.5B parameters, it may not perform as robustly as larger models.
+- **Training Duration:** Fine-tuning was performed for a single epoch; further training might be needed for more challenging tasks.
+- **Reward Function Limitations:** The custom reward functions (checking for correct formatting and numerical correctness) are heuristic and may occasionally miss subtleties in reasoning.
+- **Generalization:** The structured format (with `<reasoning>` and `<answer>` tags) is enforced during training and may require adaptation for other use cases.
 ### Recommendations
+Users should:
+- Validate model outputs on a case-by-case basis.
+- Consider further fine-tuning for domain-specific applications.
+- Use the model as a supplementary tool rather than the sole resource for critical math reasoning tasks.
 ## How to Get Started with the Model
+Below is an example code snippet to load and use the model:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_name = "emre/Qwen-0.5B-GRPO"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to("cuda")
+# Example prompt: structured with <reasoning> and <answer> tags.
+prompt = """<reasoning>
+Step-by-step reasoning:
+</reasoning>
+<answer>
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))