---
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:** deepkawamura
- **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)

# 推論用コード

# 必要なライブラリーをインストール

```
get_ipython().run_line_magic('%capture', '')
get_ipython().system('pip install unsloth')
get_ipython().system('pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"')
get_ipython().system('pip install -U torch')
get_ipython().system('pip install -U peft')
```

# 必要なライブラリーを読み込み

```
from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re
```

# ベースとなるモデルと学習した LoRA のアダプター

```
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "deepkawamura/llm-jp-3-13b-ft04"
```

# Hugging Face Token を指定。

```
HF_TOKEN = ""
```

# unsloth の FastLanguageModel で元のモデルをロード

```
dtype = None
load_in_4bit = True

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

# 元のモデルにLoRAのアダプタを統合。

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

# タスクとなるデータを読み込む

```
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 = ""
```

# モデルを用いてタスクを推論

```
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})

json_file_id = re.sub(".*/", "", adapter_id)
with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')
```