Dongwookss
/

futfut_by_zephyr7b

@@ -1,203 +1,95 @@
 ---
-library_name: transformers
 tags:
 - unsloth
 - trl
-- sft
-license: apache-2.0
 ---
-# Model Card for Model ID
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-## Model Details
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 ---
+license: apache-2.0
+datasets:
+- Dongwookss/q_a_korean_futsal
+language:
+- ko
 tags:
 - unsloth
 - trl
+- transformer
 ---
+### 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
+  - [q_a_korean_futsal](https://huggingface.co/datasets/Dongwookss/q_a_korean_futsal)
+    - 말투 학습을 위해 '해요'체로 변환하고 인삿말을 넣어 모델 컨셉을 유지하였습니다.
+- **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/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.
+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
+)
+  ```