From 7089e3165f50432b3b941c1df42702b0444c437b Mon Sep 17 00:00:00 2001
From: sekyonda <127536312+sekyondaMeta@users.noreply.github.com>
Date: Thu, 19 Oct 2023 10:38:15 -0400
Subject: [PATCH] Update HelloLlamaLocal.ipynb
---
demo_apps/HelloLlamaLocal.ipynb | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/demo_apps/HelloLlamaLocal.ipynb b/demo_apps/HelloLlamaLocal.ipynb
index d61a246d..11171f30 100644
--- a/demo_apps/HelloLlamaLocal.ipynb
+++ b/demo_apps/HelloLlamaLocal.ipynb
@@ -6,9 +6,9 @@
"metadata": {},
"source": [
"## This demo app shows:\n",
- "* how to run Llama2 locally on a Mac using llama-cpp-python and the llama-cpp's quantized Llama2 model;\n",
- "* how to use LangChain to ask Llama general questions;\n",
- "* how to use LangChain to load a recent PDF doc - the Llama2 paper pdf - and ask questions about it. This is the well known RAG (Retrieval Augmented Generation) method to let LLM such as Llama2 be able to answer questions about the data not publicly available when Llama2 was trained, or about your own data. RAG is one way to prevent LLM's hallucination. "
+ "* How to run Llama2 locally on a Mac using llama-cpp-python and the llama-cpp's quantized Llama2 model\n",
+ "* How to use LangChain to ask Llama general questions\n",
+ "* How to use LangChain to load a recent PDF doc - the Llama2 paper pdf - and ask questions about it. This is the well known RAG (Retrieval Augmented Generation) method to let LLM such as Llama2 be able to answer questions about the data not publicly available when Llama2 was trained, or about your own data. RAG is one way to prevent LLM's hallucination"
]
},
{
--
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