初始化项目，由ModelHub XC社区提供模型

Model: Itaking/itakura-300m-pretrain-cpt-sft-dpo-model
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+  - ja
+tags:
+  - olmo
+  - dpo
+  - rlhf
+  - alignment
+  - bilingual
+pipeline_tag: text-generation
+base_model: your_username/olmo2-300m-sft
+---
+
+# OLMo-2 300M — DPO (Final Model)
+
+OLMo-2 ~300M のフルパイプライン最終モデルです。
+Pretrain → CPT → SFT → DPO の 4 ステージを経て alignment 済み。
+
+## Model Architecture
+
+| 項目 | 値 |
+|------|-----|
+| Base config | allenai/OLMo-2-0425-1B（config のみ・重みは使用せず） |
+| Parameters | ~300M |
+| hidden_size | 1024 |
+| num_hidden_layers | 16 |
+| num_attention_heads | 16 |
+| num_key_value_heads | 8 (GQA) |
+| intermediate_size | 4096 |
+| Tokenizer | allenai/OLMo-2-0425-1B |
+
+## Training Pipeline
+
+| Stage | モデル | データ | 概要 |
+|-------|--------|--------|------|
+| 1. Pretrain | ランダム初期化 | FineWeb ~1.5B tokens | 大規模コーパスで言語モデリング |
+| 2. CPT | Stage 1 出力 | FineWeb-Edu + Wikipedia JA ~0.3B tokens | 英日バイリンガル継続学習 |
+| 3. SFT | Stage 2 出力 | Tülu-3 SFT mixture 100K samples | 指示追従の習得 |
+| 4. DPO（このモデル） | Stage 3 出力 | UltraFeedback binarized | 人間の好みに合わせた alignment |
+
+## DPO Training Details
+
+| 項目 | 値 |
+|------|-----|
+| Dataset | allenai/ultrafeedback_binarized_cleaned |
+| Loss | DPO (β=0.1) |
+| Learning rate | 5e-7 (linear decay) |
+| Warmup ratio | 0.1 |
+| Epochs | 1 |
+| Hardware | NVIDIA RTX 4090 24GB |
+| Framework | TRL DPOTrainer |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "your_username/olmo2-300m-dpo"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+messages = [{"role": "user", "content": "機械学習を初心者向けに説明してください。"}]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Intended Use
+
+- 英日バイリンガルの質問応答・対話
+- LLM フルパイプライン（Pretrain → DPO）の研究・学習目的
+
+## Limitations
+
+- モデルサイズが小さく（~300M）、大規模モデルと比較して品質は限定的
+- 事実の正確性を保証しない
+- 有害コンテンツの完全な排除は保証されない
+
+## Training Data Attribution
+
+| データセット | ライセンス |
+|------------|-----------|
+| FineWeb-Edu (HuggingFaceFW) | ODC-By |
+| Wikipedia JA (Wikimedia) | CC BY-SA 4.0 |
+| Tülu-3 SFT mixture (AllenAI) | 混合 |
+| UltraFeedback binarized (AllenAI) | MIT |
+
+## License
+
+Apache 2.0

config.json

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+{
+  "_name_or_path": "checkpoints/sft",
+  "architectures": [
+    "Olmo2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "max_position_embeddings": 4096,
+  "model_type": "olmo2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 100277,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.0",
+  "use_cache": false,
+  "vocab_size": 100352
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 100257,
+  "pad_token_id": 100277,
+  "transformers_version": "4.47.0"
+}

model.safetensors

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special_tokens_map.json

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+{
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+    "single_word": false
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+}

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