testing the new one bit

.gitignore

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+ex1.py
+ex2.py

README.md

@@ -1,6 +1,6 @@
 ---
 title: Native 1-bit LLM
-emoji: 🦀
+emoji: 🫦
 colorFrom: purple
 colorTo: red
 sdk: gradio

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import uuid
+
+def load_model():
+    model_id = "microsoft/bitnet-b1.58-2B-4T"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16
+    )
+    return model, tokenizer
+
+def generate_response(user_input, system_prompt, max_new_tokens, temperature, top_p, top_k, history):
+    model, tokenizer = load_model()
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_input},
+    ]
+    
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    chat_input = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate response
+    chat_outputs = model.generate(
+        **chat_input,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        do_sample=True
+    )
+    
+    # Decode response
+    response = tokenizer.decode(chat_outputs[0][chat_input['input_ids'].shape[-1]:], skip_special_tokens=True)
+    
+    # Update history
+    history.append({"role": "user", "content": user_input})
+    history.append({"role": "assistant", "content": response})
+    return history, history
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# BitNet b1.58 2B4T Demo")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("""
+            ## About BitNet b1.58 2B4T
+            BitNet b1.58 2B4T is the first open-source, native 1-bit Large Language Model with 2 billion parameters, developed by Microsoft Research. Trained on 4 trillion tokens, it matches the performance of full-precision models while offering significant efficiency gains in memory, energy, and latency. Features include:
+            - Transformer-based architecture with BitLinear layers
+            - Native 1.58-bit weights and 8-bit activations
+            - Maximum context length of 4096 tokens
+            - Optimized for efficient inference with bitnet.cpp
+            """)
+        
+        with gr.Column():
+            gr.Markdown("""
+            ## About Tonic AI
+            Tonic AI is a vibrant community of AI enthusiasts and developers always building cool demos and pushing the boundaries of what's possible with AI. We're passionate about creating innovative, accessible, and engaging AI experiences for everyone. Join us in exploring the future of AI!
+            """)
+    
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+            system_prompt = gr.Textbox(
+                label="System Prompt",
+                value="You are a helpful AI assistant.",
+                placeholder="Enter system prompt..."
+            )
+            
+            with gr.Accordion("Advanced Options", open=False):
+                max_new_tokens = gr.Slider(
+                    minimum=10,
+                    maximum=500,
+                    value=50,
+                    step=10,
+                    label="Max New Tokens"
+                )
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature"
+                )
+                top_p = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    label="Top P"
+                )
+                top_k = gr.Slider(
+                    minimum=1,
+                    maximum=100,
+                    value=50,
+                    step=1,
+                    label="Top K"
+                )
+            
+            submit_btn = gr.Button("Send")
+        
+        with gr.Column():
+            chatbot = gr.Chatbot(label="Conversation", type="messages")
+    
+    chat_history = gr.State([])
+    
+    submit_btn.click(
+        fn=generate_response,
+        inputs=[
+            user_input,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+            chat_history
+        ],
+        outputs=[chatbot, chat_history]
+    )
+
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False)