README.md

---
base_model:
- google/gemma-2-27b
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: gemma
language:
- ja
---

# Uploaded  model

- **Developed by:** hama-jp
- **License:** Gemma Terms of Use
- **Finetuned from model :** google/gemma-2-27b :: Improved using Qwen

This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)


# output.jsonlの生成方法
```python
%%capture
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git

# Install Flash Attention 2 for softcapping support
import torch
if torch.cuda.get_device_capability()[0] >= 8:
    !pip install --no-deps packaging ninja einops "flash-attn>=2.6.3"
```

```python
from unsloth import FastLanguageModel
import torch
import json

max_seq_length = 4096 
dtype = None 
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "hama-jp/gemma2-27b-sft-241213-lora-06",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
```

```python
#@title ELYZA-tasks-100-TVの読み込み
import json

# testファイルのパスを指定
file_path = 'elyza-tasks-100-TV_0.jsonl'

# データセットの辞書を初期化
dataset_test = {}

# JSONLファイルを読み込む
with open(file_path, 'r', encoding='utf-8') as file:
    for line in file:
        # 各行をJSON形式で読み取る
        task_data = json.loads(line.strip())
        # task_idとinputを取得
        task_id = task_data.get("task_id")
        input_data = task_data.get("input")
        # task_idをキーにしてdataset_testに格納
        if task_id is not None:
            dataset_test[task_id] = {"input": input_data}

EOS_TOKEN = tokenizer.eos_token

# プロンプトテンプレート
alpaca_prompt = """### 指示
以下の入力に従って適切に処理してください。
### 入力:
{}
### 出力:
"""

# dataset_testに"text"キーを追加
for task_id, content in dataset_test.items():
    input_text = content["input"]
    prompt_text = alpaca_prompt.format(input_text) + EOS_TOKEN
    dataset_test[task_id]["text"] = prompt_text
```

```python
from unsloth import FastLanguageModel


FastLanguageModel.for_inference(model)  # Enable native 2x faster inference

def extract_response(full_text):
    """
    Extracts the response part after '### 出力:'.
    Assumes the response starts after ':\n### 出力' and removes any trailing whitespace.
    """
    response_marker = "\n### 出力:"
    if response_marker in full_text:
        return full_text.split(response_marker, 1)[-1].strip()
    return full_text.strip()

with open("output.jsonl", "w", encoding="utf-8") as outfile:
    for i in range(100):
        # Get the input text
        input_text = dataset_test[i]["text"]

        # Tokenize and move input to GPU
        inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

        # Generate output
        output = model.generate(
            **inputs,
            max_new_tokens=1024,
            temperature=0.15,
            repetition_penalty=1.05,
            use_cache=True,
            do_sample=True
        )

        # Decode output text
        decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

        # Extract only the response part
        response_only = extract_response(decoded_output)

        # Print for debugging
        print("task_id:",i)
        print("input:",dataset_test[i]["input"])
        print("output:",response_only)
        print("---")

        # Prepare a dictionary for JSONL
        result = {
            "task_id": i,
            "input": dataset_test[i]["input"],
            "output": response_only
        }

        # Save to JSONL
        outfile.write(json.dumps(result, ensure_ascii=False) + "\n")
```