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Model: Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model Source: Original Platform
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README.md
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README.md
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---
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base_model: google/gemma-2-2b
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library_name: transformers
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tags:
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- math
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- reasoning
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- sft
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- gemma
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datasets:
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- nohurry/Opus-4.6-Reasoning-3000x-filtered
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language:
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- en
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- th
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license: gemma
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---
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# Gemma-2-2B SFT Reasoning Model
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A supervised fine-tuned version of [`google/gemma-2-2b`](https://huggingface.co/google/gemma-2-2b), trained to produce structured chain-of-thought reasoning on mathematical and logical problems.
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> **Model Lineage:** This SFT model serves as the foundation for downstream training:
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> [**Phonsiri/gemma-2-2b-GRPO-Reasoning-full**](https://huggingface.co/Phonsiri/gemma-2-2b-GRPO-Reasoning-full)
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---
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## Model Highlights
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This model was trained to explicitly separate its reasoning process from its final answer, using a structured output format. It learns the **syntax and structure** of chain-of-thought reasoning before any reinforcement signal is applied.
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**Output Format:**
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| Section | Tag | Description |
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|---|---|---|
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| Chain-of-Thought | `<reasoning> ... </reasoning>` | Step-by-step internal reasoning |
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| Final Answer | `<answer> ... </answer>` | Concise final answer |
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---
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## Training Details
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### Base Model
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Fine-tuned from [`google/gemma-2-2b-it`](https://huggingface.co/google/gemma-2-2b-it) using full parameter fine-tuning (no LoRA/PEFT).
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### Datasets
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Training data was combined from the following sources:
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| Dataset | Type |
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|---|---|
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| [`nohurry/Opus-4.6-Reasoning-3000x-filtered`](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | HuggingFace — Reasoning |
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| `math_combined_2566_2567.json` | Local — Thai math problems |
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| `problems_1_5.json` | Local — Math problems |
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| `problems_6_10.json` | Local — Math problems |
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| `problems_101_125.json` | Local — Math problems |
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| `combined.json` | Local — Combined problems |
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| `all_solutions.json` | Local — Solutions |
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Epochs | 3 |
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| Learning Rate | 2e-5 |
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| LR Scheduler | Cosine |
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| Max Seq Length | 8192 |
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| Batch Size (per device) | 4 |
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| Gradient Accumulation | 4 |
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| Effective Batch Size | 16 |
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| Warmup Ratio | 0.1 |
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| Weight Decay | 0.01 |
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| Precision | bfloat16 |
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| Gradient Checkpointing | Yes |
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| Attention | SDPA |
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| Optimizer | AdamW |
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import torch
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model_id = "Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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system_prompt = (
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"You are a helpful assistant. Please reason step by step, "
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"and put your thoughts within <reasoning> and </reasoning> tags, "
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"and your final answer within <answer> and </answer> tags."
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)
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prompt = "Solve for x: 3x + 5 = 20"
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messages = [{"role": "user", "content": f"{system_prompt}\n\n{prompt}"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
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with torch.no_grad():
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model.generate(
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**inputs,
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streamer=streamer,
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max_new_tokens=4096,
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temperature=0.6,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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```
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### Example Output
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```
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<reasoning>
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We need to isolate x on one side of the equation.
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Step 1: Subtract 5 from both sides.
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3x + 5 - 5 = 20 - 5
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3x = 15
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Step 2: Divide both sides by 3.
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x = 15 / 3
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x = 5
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</reasoning>
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<answer>
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x = 5
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</answer>
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```
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---
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## Acknowledgements
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**Authors:**
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- **Phonsiri Thabunsri** — [@Phonsiriwillbejommarn](https://github.com/Phonsiriwillbejommarn)
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- **CYP777** — [@CYP777](https://github.com/CYP777)
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**Project Advisor:**
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- **Supaporn Bunrit, Ph.D.** — Suranaree University of Technology
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**Institutions & Credits:**
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- **Suranaree University of Technology (SUT)** — Research support and computing resources
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- **Google DeepMind** — Open-weights Gemma 2 model
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