---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- ja
---
# Uploaded Model

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

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Required Libraries and Their Versions

- torch>=2.3.0
- transformers>=4.40.1
- tokenizers>=0.19.1
- accelerate>=0.29.3
- flash-attn>=2.5.8

# Usage

```python
from unsloth import FastLanguageModel

model_name = "llm-jp/llm-jp-3-13b"  # モデル名
max_seq_length = 512  # 最大シーケンス長
dtype = None  # データ型（None で自動設定）
load_in_4bit = True  # 4bit量子化を使用

# モデルとトークナイザーのロード
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    token="YOUR_HUGGING_FACE_TOKEN",  # Hugging Face トークンを指定
)

# 推論用にモデルを準備
FastLanguageModel.for_inference(model)

# プロンプトの設定
prompt = "LLMとはなんですか？"

# トークナイザーで入力をエンコード
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

# モデルで生成を行う
outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True, do_sample=False, repetition_penalty=1.2)

# 出力のデコード
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
print(prediction)
```