Tomoya1999
/

llm-jp-3-13b-finetune-2

@@ -1,202 +1,95 @@
 ---
 base_model: llm-jp/llm-jp-3-13b
-library_name: peft
 ---
-# Model Card for Model ID
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 ## Model Details
-### Model Description
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-### Framework versions
-- PEFT 0.13.2

 ---
+language:
+- ja
+tags:
+- japanese
+- llm-jp
+- instruction-tuning
+- text-generation
+- unsloth
+license: other
+datasets:
+- ichikara-instruction
+model-index:
+- name: llm-jp-3-13b-finetune-2
+  results: []
 base_model: llm-jp/llm-jp-3-13b
 ---
+# llm-jp-3-13b-finetune-2
+This is a fine-tuned version of [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) using the [ichikara-instruction dataset](https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/).
 ## Model Details
+- **Base Model**: llm-jp/llm-jp-3-13b
+- **Training Type**: Instruction Fine-tuning
+- **Training Method**: QLoRA (4-bit quantization)
+- **Library Used**: unsloth
+### Training Configuration
+- **Max Sequence Length**: 512
+- **LoRA Configuration**:
+  - Rank: 32
+  - Alpha: 32
+  - Dropout: 0.05
+  - Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+### Training Hyperparameters
+- Batch Size: 2 per device
+- Gradient Accumulation Steps: 4
+- Learning Rate: 2e-4
+- Number of Epochs: 1
+- Warmup Steps: 10
+- Mixed Precision: BF16 (if supported) / FP16 (if BF16 not supported)
+## Training Data
+The model was fine-tuned on the ichikara-instruction dataset, which is a high-quality Japanese instruction dataset created by Satoshi Sekine et al. The dataset was presented at the 30th Annual Conference of the Japanese Association for Natural Language Processing (2024).
+### Input Format
+```
+### 指示
+{instruction}
+### 回答
+{response}
+```
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "your-username/llm-jp-3-13b-finetune-2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    trust_remote_code=True
+)
+# Example usage
+instruction = "ここに指示を入力してください"
+prompt = f"### 指示\n{instruction}\n### 回答\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    do_sample=True
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## References
+- Original Model: [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b)
+- Training Dataset: Sekine, S., Ando, M., Goto, M., Suzuki, K., Kawahara, D., Inui, K., & Inui, K. (2024). ichikara-instruction: Building Japanese Instruction Data for LLMs. In Proceedings of the 30th Annual Conference of the Japanese Association for Natural Language Processing.
+## License
+Please refer to the license of the original model and the training dataset.