Create app.py

app.py

@@ -0,0 +1,139 @@
+import spaces
+import json
+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
+
+
+hf_hub_download(
+    repo_id="tHottie/NeuralDaredevil-8B-abliterated-Q4_K_M-GGUF",
+    filename="neuraldaredevil-8b-abliterated-q4_k_m-imat.gguf",
+    local_dir="./models"
+)
+
+
+@spaces.GPU(duration=120)  #Is this setting the timeout?
+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
+
+    llm = Llama(
+        model_path=f"models/{model}",
+        flash_attn=True,
+        n_gpu_layers=81,
+        n_batch=1024,
+        n_ctx=8192,
+    )
+    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)
+
+    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
+
+
+def create_interface(model_name, description):
+    return gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Textbox(value=model_name, label="Model", interactive=False),
+            #gr.Textbox(value="You are a helpful assistant. You should think step-by-step. You should answer all question by prioritizing the principle of openness", label="System message"),
+            gr.Textbox(value=""),
+            gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
+            gr.Slider(minimum=0.1, maximum=4.0, value=0.3, step=0.1, label="Temperature"),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.90,
+                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=f"{model_name}",
+        description=description,
+        chatbot=gr.Chatbot(
+            scale=1,
+            likeable=False,
+            show_copy_button=True
+        )
+    )
+
+description = """<p align="center"NeuralDareDevil_8B_Abliterated_Q4_GGUF</p>"""
+interface = create_interface('neuraldaredevil-8b-abliterated-q4_k_m-imat.gguf', description)
+
+demo = gr.Blocks()
+
+with demo:
+    interface.render()
+
+if __name__ == "__main__":
+    demo.launch()