README.md

---
library_name: transformers
tags:
- transformer
- SFT
- Japanese
- trl
- text-generation-inference
license: apache-2.0
language:
- en
base_model:
- llm-jp/llm-jp-3-13b
---

# Model Card for Model ID

このモデルは東京大学松尾・岩澤研究室のLLM講座2024の最終課題のためにllm-jp/llm-jp-3-13bをファインチューニングして作成したモデルです。



## Model Details

### Model Description
・Base model：llm-jp/llm-jp-3-13b
・Dataset for SFT：ichikara-instruction
・Liscence：Apahce2.0

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

- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### 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.

## 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

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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## Glossary [optional]

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## 推論用コード
```

device = "cuda" if torch.cuda.is_available() else "cpu"
!pip install -q datasets==3.0.2
!pip install transformers==4.45.0
!pip install accelerate==1.0.1
!pip install peft==0.13.2
!pip install trl==0.11.4
!pip install bitsandbytes==0.44.1
!pip install ipywidgets --upgrade

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

HF_TOKEN = "Hugging Face Token"

model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "ryowatanabe240215/llm-jp-3-13b-finetune1214_2"

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

model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

datasets = []
with open("elyza-tasks-100-TV_0.jsonl", "r") 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 = data["input"]

  prompt = f"""以下は、タスクを説明する指示と、指示に対する回答の組み合わせです。指示を適切に満たす回答を書きなさい。
  ### 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=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, "output": output})

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

```