README.md

---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---

# Uploaded  model

- **Developed by:** holy0516
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b

This llama 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)

# 出力までの流れ
- 1. 必要なライブラリのインストール・インポート
  2. ベースモデルの読み込みとLoRAアダプタの指定
  3. Hugging Face Tokenの指定
  4. 元モデルのロード
  5. LoRAアダプタの結合
  6. タスクの読み込み
  7. 推論
  8. 出力

# コード
## 1. 必要なライブラリのインストール・インポート

!pip uninstall unsloth -y
!pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --upgrade torch
!pip install --upgrade xformers

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from unsloth import FastLanguageModel
import torch
from tqdm import tqdm
import json

## 2. ベースモデルの読み込みとLoRAアダプタの指定

model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "holy0516/llm-jp-3-13b-it-r1_elyza100-r4"

## 3. Hugging Face Tokenの指定
(略)

## 4. 元モデルのロード

dtype = None # Noneにしておけば自動で設定
load_in_4bit = True # 今回は13Bモデルを扱うためTrue

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_id,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    trust_remote_code=True,
)

# 5. LoRAのアダプタの統合

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

# 6. タスクの読み込み

datasets = []
with open("/content/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 = ""
# 7. 推論

FastLanguageModel.for_inference(model)
results = []
for dt in tqdm(datasets):
  input = dt["input"]

  prompt = f"""### 指示\n{input}\n### 回答\n"""

  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)

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

  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
        
# 8. 出力結果の保存
with open(f"output.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')