--- library_name: transformers tags: - unsloth - trl - sft datasets: - mintaeng/llm_futsaldata_yo license: apache-2.0 language: - ko --- ### Model Name : 풋풋이(futfut) #### Model Concept - 풋살 도메인 친절한 도우미 챗봇을 구축하기 위해 LLM 파인튜닝과 RAG를 이용하였습니다. - **Base Model** : [zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) - 풋풋이의 말투는 '해요'체를 사용하여 말끝에 '얼마든지 물어보세요~! 풋풋~!'로 종료합니다.

### Serving by Fast API - Git repo : [Dongwooks](https://github.com/ddsntc1/FA_Chatbot_for_API) #### Summary: - **Unsloth** 패키지를 사용하여 **LoRA** 진행하였습니다. - **SFT Trainer**를 통해 훈련을 진행 - 활용 데이터 - [llm_futsaldata_yo](https://huggingface.co/datasets/mintaeng/llm_futsaldata_yo) - 말투 학습을 위해 '해요'체로 변환하고 인삿말을 넣어 모델 컨셉을 유지하였습니다. - **Train for 7H 23M** - **Environment** : Colab 환경에서 진행하였으며 L4 GPU를 사용하였습니다. **Model Load** ``` python #!pip install transformers==4.40.0 accelerate import os import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = 'Dongwookss/big_fut_final' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) model.eval() ``` **Query** ```python from transformers import TextStreamer PROMPT = '''Below is an instruction that describes a task. Write a response that appropriately completes the request. 제시하는 context에서만 대답하고 context에 없는 내용은 모르겠다고 대답해''' messages = [ {"role": "system", "content": f"{PROMPT}"}, {"role": "user", "content": f"{instruction}"} ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] text_streamer = TextStreamer(tokenizer) _ = model.generate( input_ids, max_new_tokens=4096, eos_token_id=terminators, do_sample=True, streamer = text_streamer, temperature=0.6, top_p=0.9, repetition_penalty = 1.1 ) ``` ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** Dongwookss - **Model type:** [More Information Needed] - **Language(s) (NLP):** Korean - **Finetuned from model :** HuggingFaceH4/zephyr-7b-beta ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]