metadata

library_name: transformers
datasets:
  - llm-jp/magpie-sft-v1.0
base_model:
  - google/gemma-2-9b
license: gemma
language:
  - ja
  - en

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Model Details

Model Description

このモデルはgemma-2-9bをbitsandbytesで4bit量子化し、llm-jp/magpie-sft-v0.1を用いQloraでInstruction Turnnigしたモデルです。 loraアダプターはmssfj/gemma-2-9b-4bit-magpieになります。

以下のチャットテンプレートを定義しています。 {%- for message in messages %} {{ message.role }}: {{ message.content }} {%- endfor %}{% if add_generation_prompt %} assistant: {% endif %}

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Uses

使用方法は以下です。

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from peft import PeftModel, PeftConfig

model_name = "mssfj/gemma-2-9b-bnb-4bit-chat-template"
lora_weight = "mssfj/gemma-2-9b-4bit-magpie"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=False,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=False
)

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quantization_config,
    device_map="auto"
    )

model = PeftModel.from_pretrained(base_model, lora_weight)

tokenizer = AutoTokenizer.from_pretrained(model_name)

input="""日本で一番高い山は？
"""

messages = [
    {"role": "system", "content": """あなたは誠実で優秀な日本人のアシスタントです。あなたはユーザと日本語で会話しています。アシスタントは以下の原則を忠実に守り丁寧に回答します。
    - 日本語で簡潔に回答する
    - 回答は必ず完結した文で終える
    - 質問の文脈に沿った自然な応答をする
    """},
    {"role": "user", "content": input},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.2,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    early_stopping=True,
)

response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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