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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM |
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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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base_model_id = "mistralai/Mistral-7B-v0.1" |
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model_directory = "Tonic/mistralmed" |
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = 'left' |
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model_config = AutoConfig.from_pretrained(base_model_id) |
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peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config) |
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peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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") |
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peft_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") |
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peft_model = PeftModel.from_pretrained(base_model, "Tonic/mistralmed", token="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") |
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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, input): |
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user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt") |
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if self.history: |
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chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1) |
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else: |
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chat_history_ids = user_input_ids |
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response = peft_model.generate(chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) |
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self.history = response |
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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 = "👋🏻Welcome to Tonic's MistralMed Chat🚀" |
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description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together." |
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examples = [["What is the boiling point of nitrogen"]] |
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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", |
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outputs="text", |
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theme="ParityError/Anime" |
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) |
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iface.launch() |
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