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README.md
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[Tonic/MistralMED_Chat](https://huggingface.co/Tonic/MistralMED_Chat)
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```python
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from transformers import
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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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# Use the base model's ID
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base_model_id = "mistralai/Mistral-7B-v0.1"
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model_directory = "Tonic/mistralmed"
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# Instantiate the
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
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#tokenizer.
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tokenizer.padding_side = 'left'
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def __init__(self):
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self.history = []
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def predict(self,
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# Encode user input
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user_input_ids = tokenizer.encode(
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# Concatenate the user input with chat history
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if len(self.history) > 0:
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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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[Tonic/MistralMED_Chat](https://huggingface.co/Tonic/MistralMED_Chat)
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```python
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from transformers import AutoTokenizer, MistralForCausalLM
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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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import random
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# Functions to Wrap the Prompt Correctly
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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(input_text, system_prompt="", max_length=512):
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"""
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Generates text using a large language model, given a prompt and a device.
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Args:
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input_text: The input text to generate a response for.
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system_prompt: Optional system prompt.
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max_length: Maximum length of the generated text.
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Returns:
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A string containing the generated text.
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"""
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# Modify the input text to include the desired format
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formatted_input = f"""<s>[INST]{input_text}[/INST]"""
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# Encode the input text
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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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# Generate a response using the model
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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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# Decode the response
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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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# Define the device
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device = "cuda" if torch.cuda.is_available() else "cpu"
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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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model_directory = "Tonic/mistralmed"
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# Instantiate the Tokenizer
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
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# tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", 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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def __init__(self):
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self.history = []
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def predict(self, input_text):
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# Encode user input
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user_input_ids = tokenizer.encode(input_text, return_tensors="pt")
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# Concatenate the user input with chat history
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if len(self.history) > 0:
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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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