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Model: saadxsalman/Q-SS-0.5B-Reasoning-Math Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- qwen2.5
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- math
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- reasoning
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- grpo
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- reinforcement-learning
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- unsloth
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- gsm8k
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- structured-output
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datasets:
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- openai/gsm8k
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- open-r1/OpenR1-Math-220k
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Q-SS-0.5B-Reasoning-Math
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> *A compact, fast, and structured mathematical reasoning model — built to think before it answers.*
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**Q-SS-0.5B-Reasoning-Math** is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), trained using **Group Relative Policy Optimization (GRPO)** reinforcement learning — the same technique behind DeepSeek-R1. The model is designed to reason explicitly and transparently through mathematical problems before producing a clean, parseable final answer.
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> 💾 Looking for the lightweight CPU version? See [Q-SS-0.5B-Reasoning-Math-GGUF](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math-GGUF) for the Q4_K_M quantized model (~300MB).
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---
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## ✨ Highlights
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- 🧠 **Thinks out loud** — explicit step-by-step reasoning inside `<thought>` tags before every answer
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- 🎯 **Clean structured output** — final answer always isolated in `<answer>` tags, trivial to parse
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- 🔁 **RL-trained** — learned through reward signals, not just imitation
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- 🔧 **Fine-tunable** — full FP16 weights, ready for further training or fine-tuning
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- 🔓 **Apache 2.0** — free for personal and commercial use
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---
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## 📋 Model Details
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| Property | Details |
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|---|---|
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| **Model Name** | Q-SS-0.5B-Reasoning-Math |
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| **Base Model** | Qwen/Qwen2.5-0.5B-Instruct |
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| **Parameters** | 500M |
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| **Training Method** | SFT Warm-up + GRPO Reinforcement Learning |
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| **Trained On** | GSM8K + OpenR1-Math-220k |
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| **Precision** | FP16 (merged, no adapter needed) |
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| **License** | Apache 2.0 |
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| **Developer** | Saad Salman |
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---
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## 💬 Output Format
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Every response follows this strict structure:
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```
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<thought>
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[Step-by-step reasoning and calculations]
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</thought>
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<answer>
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[Final numerical answer only]
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</answer>
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```
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---
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "saadxsalman/Q-SS-0.5B-Reasoning-Math"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype = torch.float16,
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device_map = "auto",
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)
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SYSTEM_PROMPT = \"\"\"You are a mathematical reasoning engine.
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Solve the problem step-by-step inside <thought> tags, then give ONLY the
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final numerical or LaTeX result inside <answer> tags.
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<thought>
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[Your internal reasoning and calculations here]
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</thought>
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<answer>
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[Final answer only]
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</answer>\"\"\"
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def solve(problem):
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": problem},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize = True,
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add_generation_prompt = True,
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return_tensors = "pt",
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).to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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input_ids = inputs,
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max_new_tokens = 384,
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temperature = 0.1,
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do_sample = True,
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pad_token_id = tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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if "<answer>" in response:
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return response.split("<answer>")[-1].split("</answer>")[0].strip()
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return response
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print(solve("Janet has 3 cats. Each cat eats 2 cans of food per day. How many cans does she need for 7 days?"))
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# Output: 42
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```
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---
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## 📝 Example Outputs
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**Problem:** Janet has 3 cats. Each cat eats 2 cans of food per day. How many cans does she need for 7 days?
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```
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<thought>
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Each cat eats 2 cans per day.
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Janet has 3 cats, so they eat 3 × 2 = 6 cans per day together.
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For 7 days: 6 × 7 = 42 cans total.
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</thought>
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<answer>
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42
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</answer>
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```
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**Problem:** Tom has $50. He buys a book for $12 and a pen for $3. How much money does he have left?
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```
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<thought>
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Tom starts with $50.
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He spends $12 on a book and $3 on a pen.
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Total spent: 12 + 3 = $15.
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Money remaining: 50 - 15 = $35.
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</thought>
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<answer>
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35
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</answer>
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```
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---
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## ✅ What It's Good At
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| Problem Type | Support |
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| Basic arithmetic | ✅ Reliable |
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| Multi-step word problems | ✅ Reliable |
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| Problems with units and currency | ✅ Reliable |
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| Basic algebra | ⚠️ Partial |
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| Competition math (AMC/AIME) | ❌ Beyond capacity |
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---
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## 📦 Related Models
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| Repo | Format | Size | Best For |
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|---|---|---|---|
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| [Q-SS-0.5B-Reasoning-Math](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math) | FP16 | ~988MB | GPU inference & further fine-tuning |
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| [Q-SS-0.5B-Reasoning-Math-GGUF](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math-GGUF) | Q4_K_M | ~300MB | Local CPU inference |
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---
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## ⚠️ Limitations
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- Optimized for English language math problems only
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- Complex abstract reasoning, geometry, and calculus are beyond reliable capacity at 0.5B scale
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- Always verify critical calculations — the model may occasionally produce confident but incorrect answers
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---
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## 🙏 Acknowledgements
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- [Unsloth](https://github.com/unslothai/unsloth) — efficient fine-tuning framework
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- [Qwen Team](https://huggingface.co/Qwen) — Qwen2.5-0.5B-Instruct base model
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- [HuggingFace TRL](https://github.com/huggingface/trl) — GRPO implementation
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- [OpenR1](https://huggingface.co/open-r1) — OpenR1-Math-220k dataset
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- [OpenAI](https://huggingface.co/openai) — GSM8K dataset
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---
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## 📄 Citation
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```bibtex
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@misc{qss-reasoning-math-2025,
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author = {Saad Salman},
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title = {Q-SS-0.5B-Reasoning-Math},
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year = {2025},
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publisher = {HuggingFace},
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howpublished = {\\url{https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math}},
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}
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```
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