README.md

---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
license: apache-2.0
datasets:
- HoangHa/pensez-grpo
language:
- en
pipeline_tag: text-generation
tags:
- math
- trl
- unsloth
- grpo
- transformers
---

# Model Card for Math-RL

## Model Details

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.
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.

### Model Description


- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** Qwen2.5-0.5B-Instruct
- **Fine-tuning Method**: GRPO with LoRa
- **Domain**: Mathematics (problem-solving, reasoning)
- **Dataset Size**: ~700 examples


## Uses

### Direct Use

The model is intended for:

- Educational purposes: assisting students with math problems
- Research on small-scale RLHF-style fine-tuning (GRPO)
- Experiments in reasoning with small instruction-tuned models
- Serving as a lightweight math reasoning assistant in constrained environments


## Bias, Risks, and Limitations

- Small Dataset: Fine-tuned only on 700 math problems, so generalization is limited.
- Reasoning Errors: May produce incorrect or hallucinated answers. Always verify results.
- Not a Math Oracle: Should not be used in high-stakes scenarios (e.g., exams, grading, critical calculations).
- Limited Scope: Performance is strongest on problems similar to the fine-tuning dataset; outside domains may degrade.
- Language: While the base model supports multiple languages, math-specific fine-tuning was primarily English-based.


## How to Get Started with the Model

Use the code below to get started with the model.

```python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Math-RL")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/Math-RL",
    device_map={"": 0}
)

question = """
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 $a=\boxed{\_\_\_\_\_}$.
"""

system = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

messages = [
    {"role" : "system", "content" : system},
    {"role" : "user",   "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2048,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```