updated model

app.py

@@ -1,51 +1,39 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 class Assistant:
-    def __init__(self, model_name="ContactDoctor/Bio-Medical-Llama-3-8B"):
-            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-            self.model = AutoModelForCausalLM.from_pretrained(model_name)
-            self.pipe = pipeline("text-generation", model="ContactDoctor/Bio-Medical-Llama-3-8B")
+    def __init__(self):
+        model_name = "ruslanmv/Medical-Llama3-8B"
+        device_map = 'auto'
+        bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)
+        self.model = AutoModelForCausalLM.from_pretrained( model_name,quantization_config=bnb_config, trust_remote_code=True,use_cache=False,device_map=device_map)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
 
 
     def respond(
         self,
-        message,
-        history: list[tuple[str, str]],
-        system_message,
-        max_tokens,
-        temperature,
-        top_p,
+        message
     ):
-        messages = [{"role": "system", "content": system_message}]
-
-        for val in history:
-            if val[0]:
-                messages.append({"role": "user", "content": val[0]})
-            if val[1]:
-                messages.append({"role": "assistant", "content": val[1]})
-
-        messages.append({"role": "user", "content": message})
-
-        response = ""
-
-        for message in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            token = message.choices[0].delta.content
-
-            response += token
-            yield response
+        sys_message = ''' 
+        You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
+        provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
+        '''   
+        messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}]
+        
+        # Applying chat template
+        prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.tokenizer(prompt, return_tensors="pt").to("cuda")
+        outputs = self.model.generate(**inputs, max_new_tokens=100, use_cache=True)
+        
+        # Extract and return the generated text, removing the prompt
+        response_text = self.tokenizer.batch_decode(outputs)[0].strip()
+        answer = response_text.split('<|im_start|>assistant')[-1].strip()
+        return answer
 
 
 """
@@ -54,22 +42,9 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 assistant = Assistant()
 
 demo = gr.ChatInterface(
-    assistant.respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    assistant.respond
 )
 
 
 if __name__ == "__main__":
-    # Assistant()
     demo.launch()

requirements.txt

@@ -20,6 +20,8 @@ Jinja2==3.1.5
 markdown-it-py==3.0.0
 MarkupSafe==2.1.5
 mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.4.2
 numpy==2.2.1
 orjson==3.10.13
 packaging==24.2
@@ -44,8 +46,10 @@ shellingham==1.5.4
 six==1.17.0
 sniffio==1.3.1
 starlette==0.41.3
+sympy==1.13.1
 tokenizers==0.21.0
 tomlkit==0.13.2
+torch==2.5.1
 tqdm==4.67.1
 transformers==4.47.1
 typer==0.15.1