llm-jp-3-13b-it_lora

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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 Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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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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Technical Specifications [optional]

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Framework versions

• PEFT 0.13.2