概要　

このモデルはllama3.1-8B-instructをもとに日本語性能を高めることを目的にMergekit&ファインチューニングを用いて作成されました。

meta,ELYZA,nvidiaの皆様に感謝します。

how to use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"
text = "Vtuberとして成功するために大切な5つのことを小学生にでもわかるように教えてください。"

model_name = "DataPilot/Llama3.1-ArrowSE-v0.4"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)
model.eval()

messages = [
    {"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
    {"role": "user", "content": text},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    prompt, add_special_tokens=False, return_tensors="pt"
)

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=1200,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using meta-llama/Meta-Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

models:
  - model: meta-llama/Meta-Llama-3.1-8B-Instruct
    parameters:
      weight: 1
  - model: elyza/Llama-3-ELYZA-JP-8B
    parameters:
      weight: 0.7
  - model: nvidia/Llama3-ChatQA-1.5-8B
    parameters:
      weight: 0.15
merge_method: ties
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
parameters:
  normalize: false
dtype: bfloat16