Update README.md
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README.md
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@@ -76,26 +76,25 @@ from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoM
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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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# 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(
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"""
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Generates text using a large language model, given a
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Args:
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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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#
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formatted_input = f"
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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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@@ -119,7 +118,6 @@ def multimodal_prompt(input_text, system_prompt="", max_length=512):
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return response_text
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-
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# Define the device
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device = "cuda" if torch.cuda.is_available() else "cpu"
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@@ -133,7 +131,6 @@ tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_rem
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'left'
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-
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# Specify the configuration class for the model
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#model_config = AutoConfig.from_pretrained(base_model_id)
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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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@@ -173,14 +173,14 @@ 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
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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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from peft import PeftModel, PeftConfig
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import torch
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import gradio as gr
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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(user_input, system_prompt="You are an expert medical analyst:"):
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"""
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Generates text using a large language model, given a user input and a system prompt.
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Args:
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user_input: The user's input text to generate a response for.
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system_prompt: Optional system prompt.
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Returns:
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A string containing the generated text.
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"""
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# Combine user input and system prompt
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formatted_input = f"<s>[INST]{system_prompt} {user_input}[/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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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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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'left'
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# Specify the configuration class for the model
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#model_config = AutoConfig.from_pretrained(base_model_id)
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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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# Combine user input and system prompt
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formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
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# Encode user input
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user_input_ids = tokenizer.encode(formatted_input, 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 proper treatment for buccal herpes?"]]
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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"], # Take user input and system prompt separately
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outputs="text",
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theme="ParityError/Anime"
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)
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