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---
license: apache-2.0
datasets:
- Dongwookss/q_a_korean_futsal
language:
- ko
tags:
- unsloth
- trl
- transformer
base_model:
- HuggingFaceH4/zephyr-7b-beta
---

### Model Name : 풋풋이(futfut) 

#### Model Concept 

- ν’‹μ‚΄ 도메인 μΉœμ ˆν•œ λ„μš°λ―Έ 챗봇을 κ΅¬μΆ•ν•˜κΈ° μœ„ν•΄ LLM νŒŒμΈνŠœλ‹κ³Ό RAGλ₯Ό μ΄μš©ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
- **Base Model** : [zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 
- ν’‹ν’‹μ΄μ˜ λ§νˆ¬λŠ” 'ν•΄μš”'체λ₯Ό μ‚¬μš©ν•˜μ—¬ 말끝에 'μ–Όλ§ˆλ“ μ§€ λ¬Όμ–΄λ³΄μ„Έμš”! ν’‹ν’‹~!'둜 μ’…λ£Œν•©λ‹ˆλ‹€.

<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** νŒ¨ν‚€μ§€λ₯Ό μ‚¬μš©ν•˜μ—¬ **LoRA** μ§„ν–‰ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
- **SFT Trainer**λ₯Ό 톡해 ν›ˆλ ¨μ„ μ§„ν–‰
- ν™œμš© 데이터
  - [q_a_korean_futsal](https://huggingface.co/datasets/Dongwookss/q_a_korean_futsal)
    - 말투 ν•™μŠ΅μ„ μœ„ν•΄ 'ν•΄μš”'체둜 λ³€ν™˜ν•˜κ³  인삿말을 λ„£μ–΄ λͺ¨λΈ 컨셉을 μœ μ§€ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
   
- **Environment** : Colab ν™˜κ²½μ—μ„œ μ§„ν–‰ν•˜μ˜€μœΌλ©° L4 GPUλ₯Ό μ‚¬μš©ν•˜μ˜€μŠ΅λ‹ˆλ‹€. 

  
  **Model Load**
  
  ``` python
  
  #!pip install transformers==4.40.0 accelerate
  import os
  import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  model_id = 'Dongwookss/small_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.
μ œμ‹œν•˜λŠ” contextμ—μ„œλ§Œ λŒ€λ‹΅ν•˜κ³  context에 μ—†λŠ” λ‚΄μš©μ€ λͺ¨λ₯΄κ² λ‹€κ³  λŒ€λ‹΅ν•΄'''

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
)
  
  ```