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from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM |
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from peft import PeftModel, PeftConfig |
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import torch |
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import gradio as gr |
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import random |
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from textwrap import wrap |
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def wrap_text(text, width=90): |
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lines = text.split('\n') |
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wrapped_lines = [textwrap.fill(line, width=width) for line in lines] |
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wrapped_text = '\n'.join(wrapped_lines) |
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return wrapped_text |
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def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"): |
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formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]" |
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encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False) |
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model_inputs = encodeds.to(device) |
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output = model.generate( |
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**model_inputs, |
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max_length=max_length, |
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use_cache=True, |
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early_stopping=True, |
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bos_token_id=model.config.bos_token_id, |
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eos_token_id=model.config.eos_token_id, |
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pad_token_id=model.config.eos_token_id, |
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temperature=0.1, |
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do_sample=True |
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) |
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response_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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return response_text |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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base_model_id = "HuggingFaceH4/zephyr-7b-beta" |
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model_directory = "pseudolab/K23_MiniMed" |
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tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True, padding_side="left") |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = 'left' |
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peft_config = PeftConfig.from_pretrained("pseudolab/K23_MiniMed") |
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peft_model = MistralForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True) |
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peft_model = PeftModel.from_pretrained(peft_model, "pseudolab/K23_MiniMed") |
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class ChatBot: |
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def __init__(self): |
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self.history = [] |
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class ChatBot: |
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def __init__(self): |
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self.history = [] |
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def predict(self, user_input, system_prompt="You are an expert medical analyst:"): |
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formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]" |
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user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt") |
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response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) |
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response_text = tokenizer.decode(response[0], skip_special_tokens=True) |
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return response_text |
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bot = ChatBot() |
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title = "👋🏻토닉의 미스트랄메드 채팅에 오신 것을 환영합니다🚀👋🏻Welcome to Tonic's MistralMed Chat🚀" |
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description = "이 공간을 사용하여 현재 모델을 테스트할 수 있습니다. [pseudolab/K23_MiniMed](https://huggingface.co/pseudolab/K23_MiniMed) 또는 이 공간을 복제하고 로컬 또는 🤗HuggingFace에서 사용할 수 있습니다. [Discord에서 함께 만들기 위해 Discord에 가입하십시오](https://discord.gg/VqTxc76K3u). You can use this Space to test out the current model [pseudolab/K23_MiniMed](https://huggingface.co/pseudolab/K23_MiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)." |
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examples = [["[Question:] What is the proper treatment for buccal herpes?", "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]] |
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iface = gr.Interface( |
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fn=bot.predict, |
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title=title, |
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description=description, |
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examples=examples, |
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inputs=["text", "text"], |
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outputs="text", |
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theme="ParityError/Anime" |
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) |
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iface.launch() |