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Update app.py
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app.py
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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# api token for huggingface.co
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api_token = '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'
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# Use the base model's ID
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base_model_id = "mistralai/Mistral-7B-v0.1"
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# Create a configuration object specific to the base model (you can replace with your model's actual configuration if available)
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config = BertConfig()
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# Load the fine-tuned model "Tonic/mistralmed"
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model =
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tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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@@ -27,25 +20,25 @@ class ChatBot:
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flat_history = [item for sublist in self.history for item in sublist]
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flat_history_tensor = torch.tensor(flat_history).unsqueeze(dim=0)
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bot_input_ids = torch.cat([flat_history_tensor, new_user_input_ids], dim=-1) if self.history else new_user_input_ids
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chat_history_ids = model.generate(bot_input_ids, max_length=
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self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist()[0])
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response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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return response
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bot = ChatBot()
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title = "👋🏻Welcome to Tonic's
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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
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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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from transformers import AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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# Use the base model's ID
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base_model_id = "mistralai/Mistral-7B-v0.1"
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# Load the fine-tuned model "Tonic/mistralmed"
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model = AutoModelForCausalLM.from_pretrained("Tonic/mistralmed")
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tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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flat_history = [item for sublist in self.history for item in sublist]
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flat_history_tensor = torch.tensor(flat_history).unsqueeze(dim=0)
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bot_input_ids = torch.cat([flat_history_tensor, new_user_input_ids], dim=-1) if self.history else new_user_input_ids
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chat_history_ids = model.generate(bot_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
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self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist()[0])
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response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
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return response
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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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