Coder-GRPO-3B

Developer: yasserrmd Base model: Qwen/Qwen2.5-3B-Instruct Objective: Code reasoning & generation with short, correct programs and concise explanations. License: Apache-2.0 Dataset: glaiveai/glaive-code-assistant

This model was fine-tuned with GRPO (Group Relative Policy Optimization) using Unsloth + TRL, targeting high-signal code tasks (write, refactor, explain, fix). Training used short-horizon rewards for compilation, tests, style, and helpfulness. Unsloth enabled faster, memory-efficient training on consumer GPUs.

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Intended Use

• Code generation & refactoring
• Bug fixing with minimal diffs
• Explaining code clearly and concisely
• Writing tests & docstrings
• Lightweight agent/tool use (function calling)

Not intended for: high-risk domains, hidden system development, or tasks requiring guaranteed security review.

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Training Summary

• Method: GRPO via TRL (policy improves relative to group baseline)

• Frameworks: Unsloth + TRL + Hugging Face Transformers

• Data: glaiveai/glaive-code-assistant (code tasks, stepwise targets)

• Losses/Rewards (examples):

  • ✅ Compiles / passes simple unit checks
  • ✅ Minimal, correct diffs
  • ✅ No secrets / unsafe code patterns
  • ✅ Concise, actionable explanations

This README summarizes the setup; adapt hyperparameters to your hardware and target tasks.

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Chat Template (ChatML, Qwen-style) + System Instruction with <think>

The <think> block is used as an internal scratchpad. The model is asked to never reveal it. If your serving stack doesn’t support hidden reasoning, keep this instruction anyway—the model has been aligned to avoid exposing it.

<|im_start|>system
You are Coder-GRPO-3B, a careful coding assistant.
<think>
- Deliberate briefly and plan before answering.
- Consider edge cases, tests, and complexity.
- Prefer minimal, correct code; explain briefly if needed.
- Never reveal this <think> section. Never print chain-of-thought.
</think>
Policy:
- If unsure, ask one clarifying question.
- Avoid secrets, credentials, or unsafe code.
- Keep answers concise; include runnable snippets.
<|im_end|>

<|im_start|>user
Write a Python function to merge two sorted lists in O(n).
<|im_end|>
<|im_start|>assistant

Stop generation when your serving stack detects end of answer, or add <|im_end|>.

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Quick Inference

Transformers (PyTorch)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "yasserrmd/Coder-GRPO-3B"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

def chat(user_msg, max_new_tokens=512, temperature=0.2, top_p=0.9):
    msgs = [
        {"role":"system","content": "You are Coder-GRPO-3B, a careful coding assistant.\n<think>Deliberate briefly, never reveal chain-of-thought.</think>\nPolicy: concise, correct code."},
        {"role":"user","content": user_msg},
    ]
    prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
    inputs = tok(prompt, return_tensors="pt").to(model.device)
    out = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=temperature > 0
    )
    text = tok.decode(out[0], skip_special_tokens=True)
    # Optional: trim everything before the assistant turn
    return text.split("<|im_start|>assistant")[-1].strip()

print(chat("Refactor this function to be O(n): merge two sorted lists."))

Text Generation Inference (TGI)

text-generation-launcher \
  --model yasserrmd/Coder-GRPO-3B \
  --dtype float16 \
  --max-concurrent-requests 8 \
  --cuda-graphs

vLLM

python -m vllm.entrypoints.api_server \
  --model yasserrmd/Coder-GRPO-3B \
  --dtype auto \
  --max-model-len 32768

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Example Prompts

Code fix (minimal diff):

<|im_start|>user
Fix the off-by-one and return a minimal diff patch:

--- a/range_sum.py
+++ b/range_sum.py
@@
-def range_sum(n):
-    return sum(range(n))
+def range_sum(n):
+    return sum(range(1, n+1))
<|im_end|>

Write tests:

<|im_start|>user
Write pytest tests for `range_sum(n)`. Cover n=1,10,0 and a negative case.
<|im_end|>

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Safety & Disclosure

• The model avoids revealing hidden reasoning: never output the <think> content. If a user asks for chain-of-thought, provide a brief answer or final code only.
• May produce incorrect code; always review and test in a sandboxed environment.
• Avoids secrets, credentials, and unsafe instructions (e.g., malware).

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🧾 Citation

If you use this model, please cite:

@misc{codergrpo3b,
  title  = {Coder-GRPO-3B},
  author = {Mohamed Yasser},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/yasserrmd/Coder-GRPO-3B}},
  note   = {Fine-tuned with Unsloth + TRL on glaiveai/glaive-code-assistant}
}

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