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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: |
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- apache-2.0 |
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- cc-by-sa-4.0 |
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language: |
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- en |
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--- |
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# Uploaded model |
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- **Developed by:** RAYU555 |
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- **License:** apache-2.0 cc-by-sa-4.0 |
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- **Finetuned from model :** llm-jp/llm-jp-3-13b |
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# 出力方法 |
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下記のコードを上から実行してください。 |
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(使用ライブラリなどは適宜自身のpcにあったバージョンの物などをインストールしてから実行してください) |
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``` |
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""" |
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本リポジトリのモデルを読み込んでから実行してください |
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""" |
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import json |
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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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# 学習したモデルを用いてタスクを実行 |
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from tqdm import tqdm |
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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 = 512, 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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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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Used [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) for fineturning. |
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ELYZA-tasks-100: 日本語instructionモデル評価データセット © 2023 Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
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) |
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