Update app.py

app.py

@@ -2,55 +2,42 @@ import gradio as gr
 from llama_cpp import Llama
 from huggingface_hub import hf_hub_download
 
-# Download GGUF from your HF repo
+# Download GGUF file from HuggingFace
 model_path = hf_hub_download(
     repo_id="astegaras/Llama3.2_3B",
-    filename="model-Q2_K.gguf"
+    filename="model-Q2_K.gguf",
 )
 
-# Load model (llama.cpp)
+# Load model
 llm = Llama(
     model_path=model_path,
     n_ctx=4096,
-    chat_format=None,
     n_gpu_layers=0,
+    chat_format=None,
     add_bos_token=False,
     add_eos_token=False,
 )
 
-# Build inference prompt according to your dataset format
-def format_prompt(user_message):
-    return (
-        "<|begin_of_text|>"
-        "<|start_header_id|>system<|end_header_id|>\n"
-        "You are a helpful assistant.\n"
-        "<|start_header_id|>user<|end_header_id|>\n"
-        f"{user_message}\n"
-        "<|start_header_id|>assistant<|end_header_id|>\n"
-    )
-
-
+# EXACT SAME BEHAVIOR AS mlx_lm.generate
 def respond(user_input):
-    prompt = format_prompt(user_input)
-
     output = llm(
-        prompt,
-        max_tokens=512,
+        user_input,             # <-- only this!
+        max_tokens=256,
         temperature=0.7,
         top_p=0.9,
-        stop=["<|user|>", "<|system|>"],  # avoid looping
+        stop=None,
     )
+    
+    return output["choices"][0]["text"].strip()
 
-    return output["choices"][0]["text"]
-
-# Gradio UI
 gr.Interface(
     fn=respond,
-    inputs=gr.components.Textbox(label="Ask"),
-    outputs=gr.components.Textbox(label="Answer"),
-    title="Llama3.2-3B Fine-tuned Assistant"
+    inputs="text",
+    outputs="text",
+    title="Llama3.2-3B Fine-tuned Model"
 ).launch()
 
 
 
 
+