yossy0125/llm-jp-3-13b-it_lora

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Model Description

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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.

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

HF_TOKEN = "your token"
model_name = "llm-jp/llm-jp-3-13b"
adapter_name = "yossy0125/llm-jp-3-13b-it_lora/"

#QLoRaの量子化に合わせる
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type= "nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,

)

#BaseModel
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config = bnb_config,
    device_map="auto",
    token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True,token=HF_TOKEN)

#adapterをBaseModelに統合
model = PeftModel.from_pretrained(model,adapter_name,token=HF_TOKEN)

input = "カレーの具材は何ですか？"
prompt = f"""以下はタスクを説明する指示です。
要求を適切に満たす応答を出力しなさい。

### 指示:{input}

### 応答:
"""

tokenized_input = tokenizer.encode(prompt,add_special_tokens=False,return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(tokenized_input)
outputs = None
with torch.no_grad():
    outputs = model.generate(
        tokenized_input,
        attention_mask=attention_mask,
        max_new_tokens=2048, #生成するトークン数
        do_sample=False,
        repetition_penalty=1.2,
        pad_token_id=tokenizer.eos_token_id
    )[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):],skip_special_tokens=True)

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Training Details

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Training Procedure

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Evaluation

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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