latex support

app.py

@@ -1,3 +1,57 @@
+import os
+import json
+import subprocess
+from threading import Thread
+
+import torch
+import spaces
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
+
+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"
+EMOJI = "🤖"
+DESCRIPTION = f"This is the {MODEL_NAME} model designed for testing thinking for general AI tasks."
+
+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):
     if CHAT_TEMPLATE == "Auto":
         stop_tokens = [tokenizer.eos_token_id]
@@ -48,11 +102,22 @@ def predict(message, history, system_prompt, temperature, max_new_tokens, top_k,
         if new_token in stop_tokens:
             break
         result = "".join(outputs)
-        # Wrapping result in Markdown for LaTeX rendering
         yield f"$$ {result} $$"
 
 
-# Create Gradio interface
+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('AIDC-AI/Marco-o1')
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    quantization_config=quantization_config,
+    attn_implementation="flash_attention_2",
+)
+
 gr.ChatInterface(
     predict,
     title=EMOJI + " " + MODEL_NAME,