README.md

---
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.

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/66305fd7fdd79b4fe6d6a5e5/7UDKdaPfBJnazuIi1cUVw.png" width="400" height="400">
</p>


### 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

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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.
- **Developed by:** Dongwookss
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Korean
- **Finetuned from model :** HuggingFaceH4/zephyr-7b-beta

### Model Sources [optional]

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## Uses

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### Direct Use

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## Bias, Risks, and Limitations

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### Recommendations

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## How to Get Started with the Model

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## Training Details

### Training Data

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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