1kbooks/llm-jp-3-13b-finetuned-ver2

Model Card for Model ID

llm-jp-3-13bをichikaraデータセットでファインチューニングしたモデル

Model Details

Model Description

NEFTuneによりファインチューニングを実行

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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• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

Uses

Direct Use

model_id = "1kbooks/llm-jp-3-13b-finetuned-ver2"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", 
    bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

input  = "ここに指示を入力"
with torch.no_grad():
  prompt = f"""### 指示\n{input}\n### 回答\n"""

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

  outputs = model.generate(
      tokenized_input,
      attention_mask=attention_mask,
      max_new_tokens = 512, 
      use_cache = True, 
      do_sample=False, 
      repetition_penalty=1.2,
      pad_token_id=tokenizer.eos_token_id
  )
  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

print(prediction)

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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.

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Training Details

Training Data

• ichikara dataset

Training Procedure

• NEFTune