|
--- |
|
base_model: llm-jp/llm-jp-3-13b |
|
tags: |
|
- text-generation-inference |
|
- transformers |
|
- unsloth |
|
- llama |
|
- trl |
|
language: |
|
- ja |
|
--- |
|
|
|
# Uploaded model |
|
|
|
- **Developed by:** 154teru |
|
- **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. |
|
|
|
|
|
# LLM-JP-3-13B 推論テンプレート |
|
|
|
LLM-JP-3-13Bモデルを使用し、 |
|
GoogleColaboratoryで推論を行うためのテンプレート。 |
|
Unslothを使用。 |
|
|
|
開発方針はSFTを中心として、datasetを人間の目と感性で |
|
可能な限り一貫性を持たせた。 |
|
同時にパラメータを調整し、サンプルコードからmax_seq_length = 2048に変更。 |
|
|
|
## インストール |
|
|
|
```bash |
|
pip install unsloth |
|
pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" |
|
pip install -U torch |
|
pip install -U peft |
|
``` |
|
|
|
必要なライブラリは適宜保存してください。 |
|
```bash |
|
from unsloth import FastLanguageModel |
|
from peft import PeftModel |
|
import torch |
|
import json |
|
from tqdm import tqdm |
|
import re |
|
``` |
|
## 使用方法 |
|
|
|
1. Hugging Faceのトークンを設定 |
|
```python |
|
HF_TOKEN = "your_token_here" |
|
``` |
|
|
|
2. ベースモデルとLoRAアダプターのIDを指定 |
|
```python |
|
model_id = "llm-jp/llm-jp-3-13b" |
|
adapter_id = "154teru/llm-jp-3-13b-it15a4_fullset_lora" |
|
``` |
|
|
|
3. モデルとトークナイザーをロード |
|
```python |
|
model, tokenizer = FastLanguageModel.from_pretrained( |
|
model_name=model_id, |
|
dtype=None, |
|
load_in_4bit=True, |
|
trust_remote_code=True, |
|
) |
|
``` |
|
|
|
4. LoRAアダプターを統合 |
|
```python |
|
model = PeftModel.from_pretrained(model, adapter_id, token=HF_TOKEN) |
|
``` |
|
|
|
5. 入力データを準備 |
|
- JSONLフォーマットで、以下の構造を持つファイルを用意します： |
|
```json |
|
{ |
|
"task_id": "タスクID", |
|
"input": "入力テキスト" |
|
} |
|
``` |
|
|
|
6. 推論を実行 |
|
```python |
|
FastLanguageModel.for_inference(model) |
|
results = [] |
|
for dt in tqdm(datasets): |
|
input = dt["input"] |
|
prompt = f"""以下は、タスクを説明する指示です。要求を適切に満たす回答を書きなさい。### 指示\n{input}\n### 回答\n""" |
|
# 推論処理 |
|
``` |
|
|
|
7. 結果を保存 |
|
```python |
|
json_file_id = re.sub(".*/", "", adapter_id) |
|
with open("/content/submit.jsonl", 'w', encoding='utf-8') as f: |
|
for result in results: |
|
json.dump(result, f, ensure_ascii=False) |
|
f.write('\n') |
|
``` |
|
|
|
## 出力 |
|
|
|
JSONLファイルとして保存 |
|
```json |
|
{ |
|
"task_id": "タスクID", |
|
"input": "入力テキスト", |
|
"output": "モデルの出力" |
|
} |
|
``` |
|
|
|
## Sample code(Google Colab) |
|
``` |
|
model_id = "llm-jp/llm-jp-3-13b" |
|
adapter_id = "154teru/llm-jp-3-13b-it15a4_fullset2048_lora" |
|
|
|
#huggingface TOKEN |
|
HF_TOKEN = "" |
|
|
|
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, |
|
) |
|
|
|
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN) |
|
|
|
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 = "" |
|
|
|
FastLanguageModel.for_inference(model) |
|
|
|
results = [] |
|
for dt in tqdm(datasets): |
|
input = dt["input"] |
|
|
|
prompt = f"""以下は、タスクを説明する指示です。要求を適切に満たす回答を書きなさい。### 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}) |
|
|
|
with open("/content/submit.jsonl", 'w', encoding='utf-8') as f: |
|
for result in results: |
|
json.dump(result, f, ensure_ascii=False) |
|
f.write('\n') |
|
``` |
