Create app.py

app.py

@@ -0,0 +1,181 @@
+# Use TPU v2
+!pip install gradio spaces llama-cpp-python llama-cpp-agent huggingface_hub
+import spaces
+import subprocess
+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
+from google.colab import userdata
+
+huggingface_token = userdata.get("HF_TOKEN")
+
+# Download the Meta-Llama-3.1-8B-Instruct model
+hf_hub_download(
+    repo_id="bartowski/Meta-Llama-3.1-8B-Instruct-GGUF",
+    filename="Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/Mistral-Nemo-Instruct-2407-GGUF",
+    filename="Mistral-Nemo-Instruct-2407-Q5_K_M.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/gemma-2-2b-it-GGUF",
+    filename="gemma-2-2b-it-Q6_K_L.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/openchat-3.6-8b-20240522-GGUF",
+    filename="openchat-3.6-8b-20240522-Q6_K.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+hf_hub_download(
+    repo_id="bartowski/Llama-3-Groq-8B-Tool-Use-GGUF",
+    filename="Llama-3-Groq-8B-Tool-Use-Q6_K.gguf",
+    local_dir="./models",
+    token=huggingface_token
+)
+
+
+llm = None
+llm_model = None
+
+@spaces.GPU(duration=50)
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    model,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    top_k,
+    repeat_penalty,
+):
+    chat_template = MessagesFormatterType.GEMMA_2
+
+    global llm
+    global llm_model
+
+    # Load model only if it's not already loaded or if a new model is selected
+    if llm is None or llm_model != model:
+        try:
+            llm = Llama(
+                model_path=f"models/{model}",
+                flash_attn=True,
+                n_gpu_layers=81,  # Adjust based on available GPU resources
+                n_batch=1024,
+                n_ctx=8192,
+            )
+            llm_model = model
+        except Exception as e:
+            return f"Error loading model: {str(e)}"
+
+    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()
+
+    # Add user and assistant messages to the history
+    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)
+
+    # Stream the response
+    try:
+        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
+            yield outputs
+    except Exception as e:
+        yield f"Error during response generation: {str(e)}"
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Dropdown([
+                'Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf',
+                'Mistral-Nemo-Instruct-2407-Q5_K_M.gguf',
+                'gemma-2-2b-it-Q6_K_L.gguf',
+                'openchat-3.6-8b-20240522-Q6_K.gguf',
+                'Llama-3-Groq-8B-Tool-Use-Q6_K.gguf'
+            ],
+            value="gemma-2-2b-it-Q6_K_L.gguf",
+            label="Model"
+        ),
+        gr.Textbox(value="You are a helpful assistant.", label="System message"),
+        gr.Slider(minimum=1, maximum=4096, value=2048, 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=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p",
+        ),
+        gr.Slider(
+            minimum=0,
+            maximum=100,
+            value=40,
+            step=1,
+            label="Top-k",
+        ),
+        gr.Slider(
+            minimum=0.0,
+            maximum=2.0,
+            value=1.1,
+            step=0.1,
+            label="Repetition penalty",
+        ),
+    ],
+    retry_btn="Retry",
+    undo_btn="Undo",
+    clear_btn="Clear",
+    submit_btn="Send",
+    title="Chat with lots of Models and LLMs using llama.cpp",
+    chatbot=gr.Chatbot(
+        scale=1,
+        likeable=False,
+        show_copy_button=True
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch()