mssfj/gemma-2-9b-4bit-magpie

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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した loraアダプターになります。

ベースモデルはmssfj/gemma-2-9b-bnb-4bit-chat-templateになります。

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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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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

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Summary

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Environmental Impact

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

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Framework versions

• PEFT 0.13.2