--- library_name: transformers tags: - llm-jp - japanese - instruction-tuning --- # Model Card for yuhkis/llm-jp-3-13b-finetune ## Model Details ### Model Description This is a LoRA-tuned version of LLM-jp-3-13b, fine-tuned on the Ichikara Instruction dataset. - **Developed by:** Yuhki Shiraishi - **Model type:** Instruction-tuned Japanese Language Model - **Language:** Japanese - **License:** CC-BY-NC-SA - **Finetuned from model:** llm-jp/llm-jp-3-13b ## Uses ### Output Generation and Format #### Implementation Details To generate output in the required JSONL format: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch from tqdm import tqdm import json # Load model and tokenizer model_id = "yuhkis/llm-jp-3-13b-finetune" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", token=HF_TOKEN ) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token=HF_TOKEN) # Generate outputs results = [] for data in tqdm(datasets): input = data["input"] prompt = f"""### 指示 {input} ### 回答 """ tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) attention_mask = torch.ones_like(tokenized_input) with torch.no_grad(): outputs = model.generate( tokenized_input, attention_mask=attention_mask, max_new_tokens=100, do_sample=False, repetition_penalty=1.2, pad_token_id=tokenizer.eos_token_id )[0] output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True) results.append({"task_id": data["task_id"], "output": output}) # Save results to JSONL file with open("results.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n') ``` #### Output Format Specification Required fields in the JSONL output: - task_id: Task identifier (integer) - output: Generated response (string) Example output format: ```json {"task_id": 0, "output": "応答テキスト"} ``` Note: While additional fields (e.g., input, eval_aspect) may be included, only task_id and output are required for submission. ``` ### Out-of-Scope Use This model should not be used for: - Commercial applications due to license restrictions - Critical decision-making without human oversight - Applications requiring strict reliability guarantees ## Bias, Risks, and Limitations - The model inherits biases from its training data - Output quality may vary depending on input complexity - The model should not be used for making critical decisions without human oversight ### Recommendations Users should be aware of the model's limitations and verify outputs when used in applications. ## Training Details ### Training Data - Dataset: Ichikara Instruction Dataset ### Training Procedure - **Training regime:** bf16 mixed precision - **Library:** 🤗 Transformers - **Optimization:** LoRA (Low-Rank Adaptation) ## Technical Specifications ### Model Architecture - Base model: LLM-jp-3-13b - Adaptation method: LoRA ## Citation **BibTeX:** ```bibtex @misc{shiraishi2024llm, title={LLM-jp-3-13b-finetune: Instruction-tuned Japanese Language Model}, author={Yuhki Shiraishi}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/yuhkis/llm-jp-3-13b-finetune}} } ``` **Base Model Citation:** ```bibtex @misc{llm-jp2024, title={LLM-jp-3: Large Language Model for Japanese}, author={LLM-jp Project Team}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/llm-jp/llm-jp-3-13b}} } ``` **Training Data Citation:** ``` 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. 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) ``` ## Model Card Contact **Primary Contact:** - Name: Yuhki Shiraishi - GitHub: [@yuhkis](https://github.com/yuhkis) For questions regarding this model, please open an issue in the GitHub repository or contact via HuggingFace discussion forum. Please include "LLM-jp-3-13b-finetune" in the subject line of any correspondence.