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Model: khazarai/Math-RL
Source: Original Platform

README.md

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+---
+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),
+)
+```