gemma-2-9b-cb-ichikara-it

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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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Uses

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Sample Use

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Hugging Faceトークンの設定
HF_TOKEN = "あなたのトークン"

# モデルの設定
model_name = "10432mztn/gemma-2-9b-cb-ichikara-it"
commit_hash = "143b6959a349f1b7650a976387bd00c902dc5e7c"  # v3
version_name = "v3"

# 4ビット量子化の設定
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=False,
)

# モデルとトークナイザーの読み込み
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    revision=commit_hash,
    quantization_config=bnb_config,
    device_map="auto",
    token=HF_TOKEN
)

tokenizer = AutoTokenizer.from_pretrained(
    model_name, 
    trust_remote_code=True, 
    token=HF_TOKEN
)

# 推論用のデータセット読み込み
datasets = []
with open("path/to/your/tasks.jsonl", "r") as f:
    item = ""
    for line in f:
        line = line.strip()
        item += line
        if item.endswith("}"):
            datasets.append(json.loads(item))
            item = ""

# 推論の実行
results = []
for data in tqdm(datasets):
    input = data["input"]
    prompt = f"""### 指示
    {input}
    ### 回答：
    """
    
    input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **input_ids,
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.2,
    )
    output = tokenizer.decode(outputs[0][input_ids.input_ids.size(1):], skip_special_tokens=True)
    
    results.append({
        "task_id": data["task_id"],
        "input": input,
        "output": output
    })

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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

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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

Testing Data

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Factors

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Summary

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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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