---
library_name: transformers
tags: []
---
# r-yuba62/llm-jp-3-13b-finetune

## Summary

This model was created as part of the creation of a submitted model for the competition of [the Matsuo Lab Large Scale Language Modeling Course 2024]
(https://weblab.t.u-tokyo.ac.jp/lecture/course-list/large-language-model/) .

## Uploaded model

Developed by: r-yuba
License: apache-2.0
Finetuned from model : llm-jp/llm-jp-3-13b

## How to Usage

### To Install Packages

```python
!pip install -U transformers
!pip install -U bitsandbytes
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install -U trl==0.12.0
```

### Methods of Inference

```python
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from peft import PeftModel
import torch

HF_TOKEN = "AVAILABLE YOUR-HF-TOKEN"

model_name = "llm-jp/llm-jp-3-13b"
adapter_name = "r-yuba62/llm-jp-3-13b-finetune"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# モデルとトークナイザーをロード
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    token=HF_TOKEN
)

tokenizer = AutoTokenizer.from_pretrained(
    model_name, 
    trust_remote_code=True, 
    token=HF_TOKEN
)

# PEFTアダプターを適用
model = PeftModel.from_pretrained(model, adapter_name, token=HF_TOKEN)

def get_response(input_text):
    prompt = f"""### 指示
    {input_text}
    ### 回答：
    """
    # トークナイズ処理
    tokenized_input = tokenizer(
        prompt,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=512
    )

    input_ids = tokenized_input["input_ids"].to(model.device)
    attention_mask = tokenized_input["attention_mask"].to(model.device)

    # モデル生成
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_new_tokens=512,
            do_sample=False,
            repetition_penalty=1.2,
            pad_token_id=tokenizer.eos_token_id
        )

    # 出力のデコード
    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return output_text

input_text = "xxxを教えてください"
response = get_response(input_text)
print(response)
```

## Base Model

base_model:- llm-jp/llm-jp-3-13b
---

### Instruction tuning

The models have been fine-tuned on the following datasets.
 
| Language | Dataset | description | 
|:---|:---|:---|
|Japanese|[ichikara-instruction-003-001-1.json](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF-%E5%85%AC%E9%96%8B/)| A manually constructed instruction dataset |

データセット作成チーム：
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)

## License

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)