metadata

license: cc-by-nc-sa-4.0
language:
  - ja
base_model:
  - llm-jp/llm-jp-3-13b

Fine-tuned Japanese Instruction Model

This is a fine-tuned version of the base model llm-jp/llm-jp-3-13b using the ichikara-instruction dataset.
The model has been fine-tuned for Japanese instruction-following tasks.

Model Information

Base Model

Model: llm-jp/llm-jp-3-13b
Architecture: Causal Language Model
Parameters: 13 billion

Fine-tuning Dataset

Dataset: ichikara-instruction
Authors: 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
License: CC-BY-NC-SA 4.0

The dataset includes Japanese instruction-response pairs and has been tailored for Japanese instruction-following tasks.

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)

Usage

1. Install Required Libraries

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft

2. Load the Model and Libraries

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

# Hugging Face Token (recommended to set via environment variable)
HF_TOKEN = "YOUR_HF_ACCESS_TOKEN"

# Model and adapter IDs
base_model_id = "llm-jp/llm-jp-3-13b"  # Base model
adapter_id = "sasakipeter/llm-jp-3-13b-finetune"

# QLoRA (4-bit quantization) configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

3. Load the Base Model and LoRA Adapter

# Load base model with 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token=HF_TOKEN
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    base_model_id, 
    trust_remote_code=True, 
    token=HF_TOKEN
)

# Integrate LoRA adapter into the base model
model = PeftModel.from_pretrained(model, adapter_id, token=HF_TOKEN)

4. Perform Inference

# Example input prompt
input_text = """次の文章を要約してください。

日本は四季があり、春には桜が咲き、夏には暑さが続きます。秋には紅葉が美しく、冬には雪が降ります。"""

# Format the input prompt
prompt = f"""### 指示
{input_text}
### 回答
"""

# Tokenize input and move to the model's device
tokenized_input = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate output
with torch.no_grad():
    outputs = model.generate(
        **tokenized_input,
        max_new_tokens=100,
        do_sample=False,
        repetition_penalty=1.2,
        pad_token_id=tokenizer.eos_token_id
    )

# Decode the output
output = tokenizer.decode(outputs[0][tokenized_input.input_ids.size(1):], skip_special_tokens=True)
print("Output:")
print(output)

License

This model is released under the CC-BY-NC-SA 4.0 license.

Base Model: llm-jp/llm-jp-3-13b (Apache License 2.0)
Fine-Tuning Dataset: ichikara-instruction (CC-BY-NC-SA 4.0)

Fine-tuned Model License:
Due to the Share-Alike (SA) condition of the ichikara-instruction dataset, the fine-tuned model is licensed under CC-BY-NC-SA 4.0.
This means the model can only be used for non-commercial purposes, and any derivative works must adopt the same license.