Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model

Gemma-2-2B SFT Reasoning Model

A supervised fine-tuned version of google/gemma-2-2b, trained to produce structured chain-of-thought reasoning on mathematical and logical problems.

Model Lineage: This SFT model serves as the foundation for downstream training: Phonsiri/gemma-2-2b-GRPO-Reasoning-full

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Model Highlights

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.

Output Format:

Section	Tag	Description
Chain-of-Thought	<reasoning> ... </reasoning>	Step-by-step internal reasoning
Final Answer	<answer> ... </answer>	Concise final answer

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Training Details

Base Model

Fine-tuned from google/gemma-2-2b-it using full parameter fine-tuning (no LoRA/PEFT).

Datasets

Training data was combined from the following sources:

Dataset	Type
nohurry/Opus-4.6-Reasoning-3000x-filtered	HuggingFace — Reasoning
math_combined_2566_2567.json	Local — Thai math problems
problems_1_5.json	Local — Math problems
problems_6_10.json	Local — Math problems
problems_101_125.json	Local — Math problems
combined.json	Local — Combined problems
all_solutions.json	Local — Solutions

Hyperparameters

Parameter	Value
Epochs	3
Learning Rate	2e-5
LR Scheduler	Cosine
Max Seq Length	8192
Batch Size (per device)	4
Gradient Accumulation	4
Effective Batch Size	16
Warmup Ratio	0.1
Weight Decay	0.01
Precision	bfloat16
Gradient Checkpointing	Yes
Attention	SDPA
Optimizer	AdamW

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

model_id = "Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

system_prompt = (
    "You are a helpful assistant. Please reason step by step, "
    "and put your thoughts within <reasoning> and </reasoning> tags, "
    "and your final answer within <answer> and </answer> tags."
)
prompt = "Solve for x: 3x + 5 = 20"

messages = [{"role": "user", "content": f"{system_prompt}\n\n{prompt}"}]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)

with torch.no_grad():
    model.generate(
        **inputs,
        streamer=streamer,
        max_new_tokens=4096,
        temperature=0.6,
        top_p=0.9,
        repetition_penalty=1.1,
    )

Example Output

<reasoning>
We need to isolate x on one side of the equation.

Step 1: Subtract 5 from both sides.
  3x + 5 - 5 = 20 - 5
  3x = 15

Step 2: Divide both sides by 3.
  x = 15 / 3
  x = 5
</reasoning>

<answer>
x = 5
</answer>

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Acknowledgements

Authors:

• Phonsiri Thabunsri — @Phonsiriwillbejommarn
• CYP777 — @CYP777

Project Advisor:

• Supaporn Bunrit, Ph.D. — Suranaree University of Technology

Institutions & Credits:

• Suranaree University of Technology (SUT) — Research support and computing resources
• Google DeepMind — Open-weights Gemma 2 model