Update app.py

app.py

@@ -1 +1,188 @@
-import gradio as grfrom huggingface_hub import InferenceClient"""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")def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p,): 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"""For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface"""demo = gr.ChatInterface( 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)", ), ],)if __name__ == "__main__": demo.launch()
+import json
+import subprocess
+import time
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+llm = None
+llm_model = None
+
+# Download the new model
+hf_hub_download(
+    repo_id="hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF",
+    filename="llama-3.2-1b-instruct-q4_k_m.gguf",
+    local_dir="./models"
+)
+
+def get_messages_formatter_type(model_name):
+    return MessagesFormatterType.LLAMA_3
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    model,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    top_k,
+    repeat_penalty,
+):
+    global llm
+    global llm_model
+    
+    chat_template = get_messages_formatter_type(model)
+    
+    if llm is None or llm_model != model:
+        llm = Llama(
+            model_path=f"models/{model}",
+            n_gpu_layers=0,  # Adjust based on your GPU
+            n_batch=32398,     # Adjust based on your RAM
+            n_ctx=512,      # Adjust based on your RAM and desired context length
+        )
+        llm_model = model
+    
+    provider = LlamaCppPythonProvider(llm)
+
+    agent = LlamaCppAgent(
+        provider,
+        system_prompt=f"{system_message}",
+        predefined_messages_formatter_type=chat_template,
+        debug_output=True
+    )
+    
+    settings = provider.get_provider_default_settings()
+    settings.temperature = temperature
+    settings.top_k = top_k
+    settings.top_p = top_p
+    settings.max_tokens = max_tokens
+    settings.repeat_penalty = repeat_penalty
+    settings.stream = True
+
+    messages = BasicChatHistory()
+
+    for msn in history:
+        user = {
+            'role': Roles.user,
+            'content': msn[0]
+        }
+        assistant = {
+            'role': Roles.assistant,
+            'content': msn[1]
+        }
+        messages.add_message(user)
+        messages.add_message(assistant)
+    
+    start_time = time.time()
+    token_count = 0
+
+    stream = agent.get_chat_response(
+        message,
+        llm_sampling_settings=settings,
+        chat_history=messages,
+        returns_streaming_generator=True,
+        print_output=False
+    )
+    
+    outputs = ""
+    for output in stream:
+        outputs += output
+        token_count += len(output.split()) 
+        yield outputs
+
+    end_time = time.time()
+    latency = end_time - start_time
+    speed = token_count / (end_time - start_time)
+    print(f"Latency: {latency} seconds")
+    print(f"Speed: {speed} tokens/second")
+
+description = """<p><center>
+<a href="https://huggingface.co/hugging-quants/Llama-3.2-1B-Instruct-Q4_K_M-GGUF" target="_blank">[Meta Llama 3.2 (1B)]</a>
+Meta Llama 3.2 (1B) is a multilingual large language model (LLM) optimized for conversational dialogue use cases, including agentic retrieval and summarization tasks. It outperforms many open-source and closed chat models on industry benchmarks, and is intended for commercial and research use in multiple languages.
+</center></p>
+"""
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Dropdown([
+                "llama-3.2-1b-instruct-q4_k_m.gguf"
+            ],
+            value="llama-3.2-1b-instruct-q4_k_m.gguf",
+            label="Model"
+        ),
+        gr.TextArea(value="""You are Meta Llama 3.2 (1B), an advanced AI assistant created by Meta. Your capabilities include:
+1. Complex reasoning and problem-solving
+2. Multilingual understanding and generation
+3. Creative and analytical writing
+4. Code understanding and generation
+5. Task decomposition and step-by-step guidance
+6. Summarization and information extraction
+Always strive for accuracy, clarity, and helpfulness in your responses. If you're unsure about something, express your uncertainty. Use the following format for your responses:
+""", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=2.0,
+            value=0.9,
+            step=0.05,
+            label="Top-p",
+        ),
+        gr.Slider(
+            minimum=0,
+            maximum=100,
+            value=1,
+            step=1,
+            label="Top-k",
+        ),
+        gr.Slider(
+            minimum=0.0,
+            maximum=2.0,
+            value=1.1,
+            step=0.1,
+            label="Repetition penalty",
+        ),
+    ],
+    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray",font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
+        body_background_fill_dark="#16141c",
+        block_background_fill_dark="#16141c",
+        block_border_width="1px",
+        block_title_background_fill_dark="#1e1c26",
+        input_background_fill_dark="#292733",
+        button_secondary_background_fill_dark="#24212b",
+        border_color_accent_dark="#343140",
+        border_color_primary_dark="#343140",
+        background_fill_secondary_dark="#16141c",
+        color_accent_soft_dark="transparent",
+        code_background_fill_dark="#292733",
+    ),
+    title="Meta Llama 3.2 (1B)",
+    description=description,
+    chatbot=gr.Chatbot(
+        scale=1, 
+        likeable=True,
+        show_copy_button=True
+    ),
+    examples=[
+        ["Hello! Can you introduce yourself?"],
+        ["What's the capital of France?"],
+        ["Can you explain the concept of photosynthesis?"],
+        ["Write a short story about a robot learning to paint."],
+        ["Explain the difference between machine learning and deep learning."],
+        ["Summarize the key points of climate change and its global impact."],
+        ["Explain quantum computing to a 10-year-old."],
+        ["Design a step-by-step meal plan for someone trying to lose weight and build muscle."]
+    ],
+    cache_examples=False,
+    autofocus=False,
+    concurrency_limit=None
+)
+
+if __name__ == "__main__":
+    demo.launch()