Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+import torch
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+
+MODEL_ID = "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated"
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+print("Loading model (4-bit quantized)...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+    ),
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+print("Model loaded!")
+
+
+def chat(message, history):
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    for msg in history:
+        messages.append({"role": msg["role"], "content": msg["content"]})
+    messages.append({"role": "user", "content": message})
+
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
+    )
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    thread = Thread(
+        target=model.generate,
+        kwargs=dict(
+            **inputs,
+            max_new_tokens=2048,
+            temperature=0.7,
+            top_k=20,
+            top_p=0.95,
+            do_sample=True,
+            streamer=streamer,
+        ),
+    )
+    thread.start()
+
+    partial = ""
+    for token in streamer:
+        partial += token
+        yield partial
+
+
+demo = gr.ChatInterface(
+    chat,
+    title="Huihui-Qwen3.5-35B-A3B Abliterated",
+    description="Chat with the abliterated Qwen3.5-35B-A3B model (4-bit quantized, uncensored)",
+    type="messages",
+)
+demo.launch(server_name="0.0.0.0", server_port=7860)