Update README.md

README.md

@@ -66,6 +66,81 @@ Use the code below to get started with the model.
 
 [Tonic/MistralMED_Chat](https://huggingface.co/Tonic/MistralMED_Chat)
 
+```python
+from transformers import  AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+from peft import PeftModel, PeftConfig
+import torch
+import gradio as gr
+  
+  
+# Use the base model's ID
+base_model_id = "mistralai/Mistral-7B-v0.1"
+model_directory = "Tonic/mistralmed"
+
+# Instantiate the Models
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
+#tokenizer.pad_token = tokenizer.eos_token
+#tokenizer.padding_side = 'left'
+
+
+# Specify the configuration class for the model
+#model_config = AutoConfig.from_pretrained(base_model_id)
+
+# Load the PEFT model with the specified configuration
+#peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config)
+
+# Load the PEFT model
+peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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")
+peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
+peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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")
+
+class ChatBot:
+    def __init__(self):
+        self.history = []
+
+    def predict(self, input):
+        # Encode user input
+        user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
+
+        # Concatenate the user input with chat history
+        if self.history:
+            chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1)
+        else:
+            chat_history_ids = user_input_ids
+
+        # Generate a response using the PEFT model
+        # response = peft_model.generate(chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
+        # response = peft_model.generate(chat_history_ids)  
+        response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
+
+
+
+        # Update chat history
+        self.history = chat_history_ids
+
+        # Decode and return the response
+        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+        return response_text
+
+bot = ChatBot()
+
+title = "👋🏻Welcome to Tonic's MistralMed Chat🚀"
+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."
+examples = [["What is the boiling point of nitrogen"]]
+
+iface = gr.Interface(
+    fn=bot.predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs="text",
+    outputs="text",
+    theme="ParityError/Anime"
+)
+
+iface.launch()
+```
+
 ## Training Details
 
 ### Training Data