local app

app.py

@@ -32,9 +32,16 @@ hf_hub_download(
     local_dir="./models",
     token=huggingface_token
 )
+# 5GB
+
+
+# RichardErkhov/ibm-granite_-_granite-7b-base-gguf
+# granite-7b-base.Q4_K_M.gguf
+# 4GB
 
 # TobDeBer/granite-8b-code-instruct-128k-Q4_K_M-GGUF
 # granite-8b-code-instruct-128k-q4_k_m.gguf
+# 5GB
 
 llm = None
 llm_model = None
@@ -109,8 +116,8 @@ def respond(
         outputs += output
         yield outputs
 
-description = """<p align="center">Defaults to Qwen 500M</p>
-More models in Advanced Section <br>
+description = """<p align="center">Defaults to Qwen 500M<br>
+More models in Advanced Section <br></p>
 """
 
 demo = gr.ChatInterface(
@@ -153,7 +160,7 @@ demo = gr.ChatInterface(
     undo_btn="Undo",
     clear_btn="Clear",
     submit_btn="Send",
-    title="Chat with Qwen 2 using llama.cpp", 
+    title="Chat with Qwen 2 and friends using llama.cpp", 
     description=description,
     chatbot=gr.Chatbot(
         scale=1, 

app_local.py

@@ -0,0 +1,192 @@
+# conda activate audio
+# pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
+import llama_cpp
+import os
+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
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+#hf_hub_download(
+#    repo_id="Qwen/Qwen2-0.5B-Instruct-GGUF",
+#    filename="qwen2-0_5b-instruct-q4_k_m.gguf",
+#    local_dir="./models"
+#)
+
+#hf_hub_download(
+#    repo_id="TobDeBer/gpt2-Q4_K_M-GGUF",
+#    filename="gpt2-q4_k_m.gguf",
+#    local_dir="./models"
+#)
+
+#hf_hub_download(
+#    repo_id="TobDeBer/Meta-Llama-3.1-8B-Instruct-Q4_K_M-GGUF",
+#    filename="meta-llama-3.1-8b-instruct-q4_k_m.gguf",
+#    local_dir="./models",
+#    token=huggingface_token
+#)
+# 5GB
+
+
+# RichardErkhov/ibm-granite_-_granite-7b-base-gguf
+# granite-7b-base.Q4_K_M.gguf
+# 4GB
+
+# TobDeBer/granite-8b-code-instruct-128k-Q4_K_M-GGUF
+# granite-8b-code-instruct-128k-q4_k_m.gguf
+# 5GB
+
+llm = None
+llm_model = None
+
+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
+    
+    if llm is None or llm_model != model:
+        llm = Llama(
+            model_path=f"models/{model}",
+            flash_attn=True,
+            n_gpu_layers=81,
+            n_batch=1024,
+            n_ctx=8192,
+        )
+        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)
+    
+    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
+
+description = """<p align="center">Defaults to Qwen 500M</p>
+"""
+
+# Create a shared state variable to store the selected model
+selected_model = gr.State("qwen2-0_5b-instruct-q4_k_m.gguf")
+
+# Create a separate interface for model selection
+model_selection = gr.Interface(
+    fn=None,  # No function needed for model selection
+    inputs=[
+        gr.Dropdown([
+            'qwen2-0_5b-instruct-q4_k_m.gguf',
+            'gpt2-q4_k_m.gguf',
+            'meta-llama-3.1-8b-instruct-q4_k_m.gguf',
+        ],
+        value="qwen2-0_5b-instruct-q4_k_m.gguf",
+        label="Model"
+        ),
+    ],
+	outputs=None,  # Add a dummy output
+    title="Select Model",
+)
+
+# Create the main chat interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+		selected_model,  # Use the state variable to receive the 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 Qwen 2 and friends using llama.cpp",
+    description=description,
+    chatbot=gr.Chatbot(
+        scale=1,
+        likeable=False,
+        show_copy_button=True
+    )
+)
+
+gr.TabbedInterface(
+  [model_selection, demo],
+  tab_names=["Model Selection", "Demo"],
+).launch()
+
+# Launch the model selection interface
+#if __name__ == "__main__":
+#    model_selection.launch()