Update app.py

app.py

@@ -1,35 +1,59 @@
+
+import json
+import subprocess
+from threading import Thread
+
 import torch
+import spaces
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
-from threading import Thread
 
-# تنظیمات مدل
-MODEL_ID = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
-CHAT_TEMPLATE = "Auto"
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+MODEL_ID = "nikravan/Marco_o1_q4"
+CHAT_TEMPLATE = "ChatML"
 MODEL_NAME = MODEL_ID.split("/")[-1]
 CONTEXT_LENGTH = 16000
 
+# Estableciendo valores directamente para las variables
+COLOR = "blue"  # Color predeterminado de la interfaz
+EMOJI = "🤖"  # Emoji predeterminado para el modelo
+DESCRIPTION = f"This is the {MODEL_NAME} model designed for testing thinking for general AI tasks."  # Descripción predeterminada
 
-COLOR = "blue"
-EMOJI = "🤖"
-DESCRIPTION = f"This is the {MODEL_NAME} model designed for testing thinking for general AI tasks."
-
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.bfloat16
-)
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID,
-    device_map="auto",
-    quantization_config=quantization_config,
-).to(device)
+latex_delimiters_set = [{
+        "left": "\\(",
+        "right": "\\)",
+        "display": False 
+    }, {
+        "left": "\\begin{equation}",
+        "right": "\\end{equation}",
+        "display": True 
+    }, {
+        "left": "\\begin{align}",
+        "right": "\\end{align}",
+        "display": True
+    }, {
+        "left": "\\begin{alignat}",
+        "right": "\\end{alignat}",
+        "display": True
+    }, {
+        "left": "\\begin{gather}",
+        "right": "\\end{gather}",
+        "display": True
+    }, {
+        "left": "\\begin{CD}",
+        "right": "\\end{CD}",
+        "display": True
+    }, {
+        "left": "\\[",
+        "right": "\\]",
+        "display": True
+    }]
 
 
+@spaces.GPU()
 def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
-   
+    # Format history with a given chat template
     if CHAT_TEMPLATE == "Auto":
         stop_tokens = [tokenizer.eos_token_id]
         instruction = system_prompt + "\n\n"
@@ -50,19 +74,19 @@ def predict(message, history, system_prompt, temperature, max_new_tokens, top_k,
         instruction += f' {message} [/INST]'
     else:
         raise Exception("Incorrect chat template, select 'Auto', 'ChatML' or 'Mistral Instruct'")
-    
+    print(instruction)
     
     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     enc = tokenizer(instruction, return_tensors="pt", padding=True, truncation=True)
-    input_ids, attention_mask = enc.input_ids.to(device), enc.attention_mask.to(device)
+    input_ids, attention_mask = enc.input_ids, enc.attention_mask
 
     if input_ids.shape[1] > CONTEXT_LENGTH:
         input_ids = input_ids[:, -CONTEXT_LENGTH:]
         attention_mask = attention_mask[:, -CONTEXT_LENGTH:]
 
     generate_kwargs = dict(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
+        input_ids=input_ids.to(device),
+        attention_mask=attention_mask.to(device),
         streamer=streamer,
         do_sample=True,
         temperature=temperature,
@@ -81,10 +105,28 @@ def predict(message, history, system_prompt, temperature, max_new_tokens, top_k,
         yield "".join(outputs)
 
 
+# Load model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    quantization_config=quantization_config,
+    attn_implementation="flash_attention_2",
+)
+
+# Create Gradio interface
 gr.ChatInterface(
     predict,
     title=EMOJI + " " + MODEL_NAME,
     description=DESCRIPTION,
+    
+
+     
     additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
     additional_inputs=[
         gr.Textbox("You are a code assistant.", label="System prompt"),
@@ -95,4 +137,4 @@ gr.ChatInterface(
         gr.Slider(0, 1, 0.95, label="Top P sampling"),
     ],
     theme=gr.themes.Soft(primary_hue=COLOR),
-).queue().launch()
+).queue().launch()