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--- |
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library_name: transformers |
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tags: |
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- Japanese |
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- Fine |
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- tuning |
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license: apache-2.0 |
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--- |
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# Model Card for Model ID |
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Taccha/llm-jp-3-13b-finetune |
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## Model Details |
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llm-jp-3 1.8B, 3.7B, 13BのsnapshotをBaseに |
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関根聡ら. 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. |
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を用いてFine tuningを行ったモデルです |
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### Model Description |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Taccha |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** En |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### How to Get Started with the Model |
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Hugging Faceにアップロードしたモデルを用いてELYZA-tasks-100-TVの出力を得るためのコードです。 |
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こちらはLoRA_template このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっております。 |
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!pip install -U bitsandbytes |
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!pip install -U transformers |
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!pip install -U accelerate |
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!pip install -U datasets |
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!pip install -U peft |
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# notebookでインタラクティブな表示を可能とする（ただし、うまく動かない場合あり） |
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!pip install ipywidgets --upgrade |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BitsAndBytesConfig, |
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) |
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from peft import PeftModel |
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import torch |
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from tqdm import tqdm |
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import json |
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# Hugging Faceで取得したTokenをこちらに貼る。 |
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HF_TOKEN = "Your－Token” |
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# ベースとなるモデルと学習したLoRAのアダプタ。 |
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# base_ |
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model_id = "llm-jp/llm-jp-3-13b" |
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adapter_id = "Taccha/llm-jp-3-13b-finetune" # こちらにアップロードしたHugging FaceのIDを指定してください。 |
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# QLoRA config |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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quantization_config=bnb_config, |
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device_map="auto", |
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token = HF_TOKEN |
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) |
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# Load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN) |
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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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# データセットの読み込み。 |
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# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。 |
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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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# llmjp |
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results = [] |
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for data in tqdm(datasets): |
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input = data["input"] |
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prompt = f"""### 指示 |
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{input} |
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### 回答 |
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""" |
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tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) |
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attention_mask = torch.ones_like(tokenized_input) |
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with torch.no_grad(): |
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outputs = model.generate( |
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tokenized_input, |
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attention_mask=attention_mask, |
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max_new_tokens=100, |
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do_sample=False, |
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repetition_penalty=1.2, |
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pad_token_id=tokenizer.eos_token_id |
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)[0] |
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output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True) |
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results.append({"task_id": data["task_id"], "input": input, "output": output}) |
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# jsolの生成 |
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import re |
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jsonl_id = re.sub(".*/", "", adapter_id) |
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with open(f"./{jsonl_id}-outputs.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) # ensure_ascii=False for handling non-ASCII characters |
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f.write('\n') |
