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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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- en |
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--- |
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# Uploaded model |
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- **Developed by:** 84basi |
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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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```python |
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%%capture |
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!pip install unsloth |
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!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" |
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from unsloth import FastLanguageModel |
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import torch |
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import json |
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model_name = "84basi/llm-jp-3-13b-finetune-2.1" |
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token = "Hugging Face Token" #@param {type:"string"} |
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max_seq_length = 2048 |
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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_name, |
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max_seq_length = max_seq_length, |
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dtype = dtype, |
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load_in_4bit = load_in_4bit, |
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token = token, |
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) |
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FastLanguageModel.for_inference(model) |
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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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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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with open(f"/content/llm-jp-3-13b-finetune-2.1_output-2.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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!pip install python-docx |
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import json |
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from docx import Document # pip install python-docxでインストールする |
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from docx.shared import Inches, Pt, RGBColor |
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from docx.enum.text import WD_ALIGN_PARAGRAPH |
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def read_jsonl_data(jsonl_path): |
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""" |
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提出用jsonlを読み、json形式で返す |
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Args: |
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jsonl_path (str): 提出用jsonlへのパス |
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Returns: |
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jsonデータ (list of dict) |
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""" |
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results = [] |
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with open(jsonl_path, 'r', encoding='utf-8') as f: |
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for line in f: |
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line = line.strip() |
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if line: |
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try: |
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results.append(json.loads(line)) |
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except json.JSONDecodeError as e: |
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print(f"JSONデコードエラー(行内容を確認してください): {e}") |
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return results |
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def json_to_word(json_data, output_file): |
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""" |
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JSONデータをWord文書に変換する |
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Args: |
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json_data (list of dict): JSONデータのリスト |
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output_file (str): 出力するWordファイルの名前 |
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""" |
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doc = Document() |
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title = doc.add_heading('LLM Output Analysis', 0) |
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title.alignment = WD_ALIGN_PARAGRAPH.CENTER |
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for item in json_data: |
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task_id = item.get("task_id", "No Task ID") |
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doc.add_heading(f'Task ID: {task_id}', level=1) |
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doc.add_heading('Input:', level=2) |
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input_text = item.get("input", "No Input") |
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input_para = doc.add_paragraph() |
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input_para.add_run(input_text).bold = False |
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doc.add_heading('Output:', level=2) |
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output_text = item.get("output", "No Output") |
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output_para = doc.add_paragraph() |
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output_para.add_run(output_text).bold = False |
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doc.add_paragraph('=' * 50) |
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doc.save(output_file) |
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jsonl_path = '/content/llm-jp-3-13b-finetune-2.1_output-2.jsonl' |
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output_file = '/content/llm-jp-3-13b-finetune-2.1_output-2.docx' |
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jsonl_data = read_jsonl_data(jsonl_path) |
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json_to_word(jsonl_data, output_file) |
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``` |