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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "NousResearch/Hermes-4-14B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def predict(message, history):
+    history.append({"role": "user", "content": message})
+    
+    input_ids = tokenizer.apply_chat_template(
+        history, add_generation_prompt=True, return_tensors="pt"
+    ).to(model.device)
+    
+    with torch.inference_mode():
+        output = model.generate(
+            input_ids,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.95,
+            top_k=40,
+            repetition_penalty=1.1,
+            do_sample=True
+        )
+    
+    # Remove input tokens from the output
+    output_ids = output[0][input_ids.shape[-1]:]
+    response = tokenizer.decode(output_ids, skip_special_tokens=True)
+    
+    history.append({"role": "assistant", "content": response})
+    return response
+
+gr.ChatInterface(
+    predict,
+    title="Hermes-4-14B Chatbot",
+    description="Chat with Hermes-4-14B, a large language model by Nous Research",
+    examples=["Hello", "Explain quantum computing in simple terms", "What is the capital of France?"]
+).launch()