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--- |
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base_model: llm-jp/llm-jp-3-13b |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: apache-2.0 |
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language: |
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- ja |
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datasets: |
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- kinokokoro/ichikara-instruction-003 |
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--- |
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# Uploaded model |
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- **Developed by:** nishimura999 |
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- **License:** apache-2.0 |
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- **Finetuned from model :** llm-jp/llm-jp-3-13b |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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# usage |
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## -import |
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```python |
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# 必要なライブラリを読み込み |
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from unsloth import FastLanguageModel |
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from peft import PeftModel |
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import torch |
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import json |
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from tqdm import tqdm |
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import re |
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``` |
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## -setting |
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```python |
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# Hugging Faceで取得したToken |
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HF_TOKEN = "{Your hugging face token}" |
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# モデルのIDと、LoRAのアダプタ名 |
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model_id = "llm-jp/llm-jp-3-13b" |
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adapter_id = "nishimura999/llm-jp-3-13b-it-v107_lora" |
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``` |
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## -load |
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```python |
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# unslothのFastLanguageModelで元のモデルをロード。 |
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dtype = None |
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load_in_4bit = True |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name=model_id, |
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dtype=dtype, |
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load_in_4bit=load_in_4bit, |
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trust_remote_code=True, |
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) |
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# 元のモデルにLoRAのアダプタを統合。 |
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model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN) |
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``` |
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## -dataset |
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```python |
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# データセットの読み込み。 |
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datasets = [] |
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with open("./elyza-tasks-100-TV_0.jsonl", "r") as f: |
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item = "" |
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for line in f: |
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line = line.strip() |
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item += line |
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if item.endswith("}"): |
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datasets.append(json.loads(item)) |
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item = "" |
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``` |
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## -generate |
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```python |
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# モデルを用いてタスクの推論。 |
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FastLanguageModel.for_inference(model) |
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results = [] |
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for dt in tqdm(datasets): |
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input = dt["input"] |
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prompt = f"""### 指示\n{input}\n### 回答\n""" |
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inputs = tokenizer([prompt], return_tensors = "pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens = 1024, use_cache = True, do_sample=False, repetition_penalty=1.2) |
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prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1] |
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results.append({"task_id": dt["task_id"], "input": input, "output": prediction}) |
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``` |
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## -output |
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```python |
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# 結果をjsonlで保存。 |
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json_file_id = re.sub(".*/", "", adapter_id) |
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with open(f"./{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f: |
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for result in results: |
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json.dump(result, f, ensure_ascii=False) |
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f.write('\n') |
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``` |
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# ref |
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### 本モデルは下記のデータを使ってファインチューニングしております。ここでデータ提供者に感謝申し上げます。 |
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(https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/) |
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関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. |
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ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024) |
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