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 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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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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