Update app.py

app.py

@@ -1,3 +1,111 @@
 import gradio as gr
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from huggingface_hub import InferenceClient
+import os
+import torch
+
+hf_token = os.environ.get("HF_TOKEN")
+model_id = "models/mistralai/Mixtral-8x7B-Instruct-v0.1"
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", token=hf_token)
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
+
+
+
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(
+    prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+    )
+    formatted_prompt = format_prompt(prompt, history)
+
+    messages = [
+        {"role": "user", "content": f"[INST] {prompt} [/INST]"}
+    ]
+
+    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
+    stream = model.generate(inputs, **generate_kwargs)
+    output = ""
+
+    decoded = tokenizer.batch_decode(stream)
+    print(decoded[0])
+    return decoded[0]
+
+
+additional_inputs=[
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=1048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+css = """
+  #mkd {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("<h1><center>Mistral 7B Instruct<h1><center>")
+    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬<h3><center>")
+    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
+    gr.ChatInterface(
+        generate,
+        additional_inputs=additional_inputs,
+        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]]
+    )
 
-gr.load("models/mistralai/Mixtral-8x7B-Instruct-v0.1").launch()
+demo.queue(concurrency_count=75, max_size=100).launch(debug=True)