Update app.py

app.py

@@ -1,25 +1,25 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from utils import build_faiss_index, retrieve
-
-# Load documents
-with open("documents/1mg_rag.txt") as f:
-    docs = [line.strip() for line in f if line.strip()]
-
-# Build FAISS index
-index, _ = build_faiss_index(docs)
-
-# Load quantized Mistral 7B
-model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
-
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-def answer_question(query):
-    context = "\n".join(retrieve(query, index, docs))
-    prompt = f"[INST] Use the following context to answer the question.\n\nContext:\n{context}\n\nQuestion: {query} [/INST]"
-    result = generator(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
-    return result[0]['generated_text']
-
-gr.Interface(fn=answer_question, inputs="text", outputs="text", title="Mistral RAG").launch()
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from utils import build_faiss_index, retrieve
+
+# Load documents
+with open("documents/1mg_rag.txt") as f:
+    docs = [line.strip() for line in f if line.strip()]
+
+# Build FAISS index
+index, _ = build_faiss_index(docs)
+
+# Load quantized Mistral 7B
+model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoGPTQForCausalLM.from_quantized(model_id, device_map="auto", trust_remote_code=True)
+
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def answer_question(query):
+    context = "\n".join(retrieve(query, index, docs))
+    prompt = f"[INST] Use the following context to answer the question.\n\nContext:\n{context}\n\nQuestion: {query} [/INST]"
+    result = generator(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
+    return result[0]['generated_text']
+
+gr.Interface(fn=answer_question, inputs="text", outputs="text", title="Mistral RAG").launch()