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Model: AaryanK/Qwen_2.5_3B_GRPO_Reasoning_XIOSERV
Source: Original Platform

README.md

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+---
+license: cc-by-nc-2.0
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+tags:
+- Reasoning
+- GRPO
+- DeepSeek
+- CoT
+- finetune
+- toggleable-reasoning
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-3B Toggleable-Reasoning (GRPO)
+
+A fine-tune of **Qwen2.5-3B-Instruct** with a **runtime-toggleable reasoning mode**: with a short system prompt the model
+thinks inside `<reasoning>` tags before answering inside `<answer>` tags; without it, the model answers directly like a normal
+instruct model. One checkpoint, two behaviours, switched at inference time.
+
+Trained and released by [Aaryan Kapoor](https://huggingface.co/AaryanK) as an independent research project.
+**Released February 16, 2025.**
+
+DOI: [10.57967/hf/5366](https://doi.org/10.57967/hf/5366)
+
+---
+
+## Toggleable reasoning
+
+| System prompt | Behaviour |
+|---|---|
+| Included (below) | Explicit `<reasoning>` … `</reasoning>` block, then `<answer>` … `</answer>` |
+| Omitted | Standard conversational response, no visible reasoning |
+
+System prompt that enables reasoning:
+
+```
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+```
+
+The switch is purely prompt-driven — no extra tokens, adapters, or generation flags are required. The model was trained so that
+the reasoning format is triggered by this instruction and remains dormant otherwise, which keeps default responses short and
+lets applications opt into chain-of-thought only where it pays off.
+
+---
+
+## Training
+
+- **Base model:** `Qwen/Qwen2.5-3B-Instruct`
+- **Method:** Group Relative Policy Optimization (GRPO) — the reinforcement-learning objective introduced in
+  [DeepSeekMath](https://arxiv.org/abs/2402.03300) and used to train DeepSeek-R1. Group-relative advantages remove the need for
+  a separate value model, which is what makes RL on reasoning practical at small scale.
+- **Rewards:** rule-based rewards for answer correctness plus format rewards for well-formed `<reasoning>` / `<answer>` structure.
+- **Efficiency:** parameter-efficient fine-tuning (QLoRA / PEFT) so the full run fits on a single consumer GPU.
+- **Goal:** reproduce the "aha-moment" behaviour of R1-style training — the model learning to reflect on and revise its own
+  intermediate steps — in a 3B model, while keeping the ability to answer plainly when reasoning is not requested.
+
+---
+
+## Files
+
+- Safetensors / PyTorch weights (transformers-compatible)
+- GGUF quantizations for [llama.cpp](https://github.com/ggml-org/llama.cpp): **F16**, **Q8_0**, **Q5_K_M**
+
+---
+
+## Usage
+
+### transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "AaryanK/Qwen_2.5_3B_GRPO_Reasoning_XIOSERV"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
+
+REASONING_PROMPT = (
+    "Respond in the following format:\n<reasoning>\n...\n</reasoning>\n<answer>\n...\n</answer>"
+)
+
+messages = [
+    {"role": "system", "content": REASONING_PROMPT},   # drop this line for direct answers
+    {"role": "user", "content": "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?"},
+]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=512)
+print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+### llama.cpp
+
+```bash
+llama-cli -m <model>_Q5_K_M.gguf \
+  --system-prompt "Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>" \
+  -p "A bat and a ball cost \$1.10 in total. The bat costs \$1.00 more than the ball. How much does the ball cost?"
+```
+
+Omit `--system-prompt` for standard instruct-style answers.
+
+---
+
+## Limitations
+
+- 3B parameters: reasoning helps most on short math, logic, and structured tasks; it does not turn the model into a frontier reasoner.
+- Reasoning traces are not guaranteed to be faithful to the final answer.
+- Inherits the base model's knowledge cutoff, biases, and safety behaviour.
+- Released under **CC BY-NC 2.0** (non-commercial).
+
+---
+
+## Citation
+
+```bibtex
+@misc{kapoor2025toggleable,
+  author = {Kapoor, Aaryan},
+  title  = {Qwen2.5-3B Toggleable-Reasoning (GRPO)},
+  year   = {2025},
+  month  = feb,
+  doi    = {10.57967/hf/5366},
+  url    = {https://huggingface.co/AaryanK/Qwen_2.5_3B_GRPO_Reasoning_XIOSERV},
+  publisher = {Hugging Face}
+}
+```
+
+Questions and feedback: open a discussion in the Community tab.