llm-jp-3-13b-finetune

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出力方法

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install trl==0.12.0

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json

HF_TOKEN = "Your Token"

# ベースモデルと利用するモデルID（Hugging Face上に公開されているもの）
base_model_id = "llm-jp/llm-jp-3-13b" 
adapter_id = "hifill/llm-jp-3-13b-finetune"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", # nf4は通常のINT4より精度が高く、ニューラルネットワークの分布に最適です
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token = HF_TOKEN
)

# モデルとトークナイザーのロード
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, token = HF_TOKEN)
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
model.to("cuda" if torch.cuda.is_available() else "cpu")

datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r", encoding="utf-8") as f:
    item = ""
    for line in f:
        line = line.strip()
        item += line
        if item.endswith("}"):
            datasets.append(json.loads(item))
            item = ""

results = []
for data in tqdm(datasets):
    input_text = data["input"]

    prompt = f"""### 指示
{input_text}
### 回答
"""

    tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
    attention_mask = torch.ones_like(tokenized_input)

    with torch.no_grad():
        outputs = model.generate(
            tokenized_input,
            attention_mask=attention_mask,
            max_new_tokens=100,
            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)
    results.append({"task_id": data["task_id"], "input": input_text, "output": output})

jsonl_id = re.sub(".*/", "", model_id)
with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')