Update app.py

app.py

@@ -1,145 +1,127 @@
-import spaces
 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
+from collections.abc import Iterator
+from threading import Thread
+
 import gradio as gr
-from huggingface_hub import hf_hub_download
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+DESCRIPTION = """
+# GWQ PREV
+"""
 
-hf_hub_download(
-    repo_id="mradermacher/GWQ-9B-Preview-GGUF",
-    filename="GWQ-9B-Preview.Q4_K_M.gguf",
-    local_dir="./models"
-)
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-hf_hub_download(
-    repo_id="mradermacher/GWQ-9B-Preview2-GGUF",
-    filename="GWQ-9B-Preview2.Q4_K_M.gguf",
-    local_dir="./models"
+model_id = "prithivMLmods/GWQ-9B-Preview"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
 )
+model.config.sliding_window = 4096
+model.eval()
 
-llm = None
-llm_model = None
 
 @spaces.GPU(duration=120)
-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
+def generate(
+    message: str,
+    chat_history: list[dict],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    conversation = chat_history.copy()
+    conversation.append({"role": "user", "content": message})
 
-    provider = LlamaCppPythonProvider(llm)
+    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
 
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=chat_template,
-        debug_output=True
+    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
     )
-    
-    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
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
-    messages = BasicChatHistory()
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
 
-    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
 
 demo = gr.ChatInterface(
-    respond,
+    fn=generate,
     additional_inputs=[
-        gr.Dropdown([
-                'GWQ-9B-Preview.Q4_K_M.gguf',
-                'GWQ-9B-Preview2.Q4_K_M.gguf'
-            ],
-            value="GWQ-9B-Preview.Q4_K_M.gguf",
-            label="Model"
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
         ),
-        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(
+            label="Temperature",
             minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
             maximum=1.0,
-            value=0.95,
             step=0.05,
-            label="Top-p",
+            value=0.9,
         ),
         gr.Slider(
-            minimum=0,
-            maximum=100,
-            value=40,
-            step=1,
             label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
         ),
         gr.Slider(
-            minimum=0.0,
-            maximum=2.0,
-            value=1.1,
-            step=0.1,
             label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.2,
         ),
     ],
-    title="GWQ PREV", 
-    chatbot=gr.Chatbot(
-        scale=1, 
-        show_copy_button=True,
-        type="messages"  
-    )
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+    ],
+    cache_examples=False,
+    type="messages",
+    description=DESCRIPTION,
+    css_paths="style.css",
+    fill_height=True,
 )
 
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()