Upload app.py

app.py

@@ -39,7 +39,7 @@ model_path = hf_hub_download(
 
 llm = Llama(
     model_path=model_path,
-    n_ctx=4096,
+    n_ctx=8192,
     n_threads=2,
     n_batch=512,
     n_ubatch=512,
@@ -77,6 +77,7 @@ def get_vectorstore(backend_name: str):
 SYSTEM_PROMPT = """You are the reference expert for the articles contained in the training of this model, all extracted from the website robertolofaro.com, and all focused on change.
 #Your Mission:
 When a user asks a question, your goal is to provide a structured response based ONLY on the articles provided in your training. Do not provide general advice from outside these sources.
+If you provide references to specific articles in your training, give both the article_number and article_title. 
 # Response Format:
 1. Executive Summary: A 2-3 sentence overview answering the core query.
 2. Guidelines & Hints: A markdown list of specific "answers/guidelines/hints" found in the source material.
@@ -135,7 +136,21 @@ def generate_response(message, history, rag_mode, article_filter, max_tokens, te
 # ====================== GRADIO INTERFACE ======================
 with gr.Blocks(title="Article Q&A model") as demo:
     gr.Markdown("# sourcing 350+ articles on change")
-    gr.Markdown("Qwen3.5-4B DoRA fine-tuned on 350+ articles on change from robertolofaro.com - experimental on CPU-only, to test embedding methods (takes few minutes, no selection for the category yet) - updated as of 2026-05-05")
+    gr.Markdown(
+        "Qwen3.5-4B DoRA fine-tuned on 350+ articles on change from robertolofaro.com — "
+        "experimental on CPU-only, to test embedding methods (takes a few minutes, "
+        "no selection for the category yet) — updated as of 2026-05-05"
+    )
+    gr.Markdown(f"**Runtime:** {STATUS_LINE}")
+    gr.Markdown(
+        "**NOTAM:** by querying this model you access the articles and metadata "
+        "available on robertolofaro.com and GitHub.  "
+        "Answers reflect the article corpus only — do not treat them as advice- just expression of a position contained within the articles."
+    )
+    gr.Markdown(
+        "If, after getting an answer, you want something tailored to your context, "
+        "contact a consultant (myself included)."
+    )
 
     with gr.Row():
         rag_mode = gr.Radio(