---
library_name: transformers
tags:
- llm-jp
- japanese
- instruction-tuning
---

# Model Card for yuhkis/llm-jp-3-13b-finetune

## Model Details

### Model Description

This is a LoRA-tuned version of LLM-jp-3-13b, fine-tuned on the Ichikara Instruction dataset.

- **Developed by:** Yuhki Shiraishi
- **Model type:** Instruction-tuned Japanese Language Model
- **Language:** Japanese
- **License:** CC-BY-NC-SA
- **Finetuned from model:** llm-jp/llm-jp-3-13b

## Uses

### Direct Use

To use this model for inference:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "yuhkis/llm-jp-3-13b-finetune"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token=HF_TOKEN)
```

### Output Format

The model outputs results in JSONL format with required fields:
- task_id: Task identifier
- output: Generated response

Example output:
```json
{"task_id": 0, "output": "応答テキスト"}
```

### Out-of-Scope Use

This model should not be used for:
- Commercial applications due to license restrictions
- Critical decision-making without human oversight
- Applications requiring strict reliability guarantees

## Bias, Risks, and Limitations

- The model inherits biases from its training data
- Output quality may vary depending on input complexity
- The model should not be used for making critical decisions without human oversight

### Recommendations

Users should be aware of the model's limitations and verify outputs when used in applications.

## Training Details

### Training Data

- Dataset: Ichikara Instruction Dataset

### Training Procedure 

- **Training regime:** bf16 mixed precision
- **Library:** 🤗 Transformers
- **Optimization:** LoRA (Low-Rank Adaptation)

## Technical Specifications

### Model Architecture

- Base model: LLM-jp-3-13b
- Adaptation method: LoRA

## Citation

**BibTeX:**
```bibtex
@misc{shiraishi2024llm,
    title={LLM-jp-3-13b-finetune: Instruction-tuned Japanese Language Model},
    author={Yuhki Shiraishi},
    year={2024},
    publisher={Hugging Face},
    howpublished={\url{https://huggingface.co/yuhkis/llm-jp-3-13b-finetune}}
}
```

**Base Model Citation:**
```bibtex
@misc{llm-jp2024,
    title={LLM-jp-3: Large Language Model for Japanese},
    author={LLM-jp Project Team},
    year={2024},
    publisher={Hugging Face},
    howpublished={\url{https://huggingface.co/llm-jp/llm-jp-3-13b}}
}
```

**Training Data Citation:**
```
関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. 
ichikara-instruction: LLMのための日本語インストラクションデータの構築. 
言語処理学会第30回年次大会(2024)
```

## Model Card Contact

**Primary Contact:**
- Name: Yuhki Shiraishi
- GitHub: [@yuhkis](https://github.com/yuhkis)

For questions regarding this model, please open an issue in the GitHub repository or contact via HuggingFace discussion forum.

Please include "LLM-jp-3-13b-finetune" in the subject line of any correspondence.