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kiwamu9/
llm-jp-3-13b-ft241215

Name: llm-jp-3-13b-ft241215
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Model Card for Model ID

Using "llm-jp/llm-jp-3-13b" as the base model, instruction tuning was performed with QLoRA on the ichikara-instruction datasets, ichikara-instruction-003-001-1.json and ichikara-instruction-003-002-1.json.

Model Details

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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How to Get Started with the Model

Use the code below to get started with the model.

python

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

model_id = "kiwamu9/llm-jp-3-13b-ft241215"

# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token = HF_TOKEN
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)

# 元のモデルにLoRAのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

# llmjp
results = []
for data in tqdm(datasets):

    input = data["input"]

    prompt = f"""### 指示
    {input}
    ### 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=256,
            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, "output": output})

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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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