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Model: Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: google/gemma-2-2b
+library_name: transformers
+tags:
+  - math
+  - reasoning
+  - sft
+  - gemma
+datasets:
+  - nohurry/Opus-4.6-Reasoning-3000x-filtered
+language:
+  - en
+  - th
+license: gemma
+---
+
+# Gemma-2-2B SFT Reasoning Model
+
+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.
+
+> **Model Lineage:** This SFT model serves as the foundation for downstream training:
+> [**Phonsiri/gemma-2-2b-GRPO-Reasoning-full**](https://huggingface.co/Phonsiri/gemma-2-2b-GRPO-Reasoning-full)
+
+---
+
+## 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 |
+
+---
+
+## Training Details
+
+### Base Model
+Fine-tuned from [`google/gemma-2-2b-it`](https://huggingface.co/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`](https://huggingface.co/datasets/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 |
+
+---
+
+## Usage
+
+```python
+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>
+```
+
+---
+
+## Acknowledgements
+
+**Authors:**
+- **Phonsiri Thabunsri** — [@Phonsiriwillbejommarn](https://github.com/Phonsiriwillbejommarn)
+- **CYP777** — [@CYP777](https://github.com/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

chat_template.jinja

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+{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}<start_of_turn>user
+{{ message['content'] }}<end_of_turn>
+{% elif message['role'] == 'assistant' or message['role'] == 'model' %}<start_of_turn>model
+{{ message['content'] }}<end_of_turn>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<start_of_turn>model
+{% endif %}

config.json

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+{
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "layer_types": [
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": 4096,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "use_bidirectional_attention": null,
+  "use_cache": false,
+  "vocab_size": 256000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "5.2.0"
+}

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "extra_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

training_args.bin

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