From 794719670e0bcffc5cd3123b97ff76b8992b487b Mon Sep 17 00:00:00 2001
From: Aaron <29749331+aarnphm@users.noreply.github.com>
Date: Fri, 4 Aug 2023 12:10:21 -0400
Subject: [PATCH] chore: update README [skip ci]

Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
---
 README.md | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 18537add..adbfc459 100644
--- a/README.md
+++ b/README.md
@@ -576,11 +576,15 @@ To quickly start a local LLM with `langchain`, simply do the following:
 ```python
 from langchain.llms import OpenLLM
 
-llm = OpenLLM(model_name="dolly-v2", model_id='databricks/dolly-v2-7b', device_map='auto')
+llm = OpenLLM(model_name="llama", model_id='meta-llama/Llama-2-7b-hf')
 
 llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?")
 ```
 
+> [!IMPORTANT]
+> By default, OpenLLM use `safetensors` format for saving models. If the model doesn't support safetensors,
+> make sure to pass `serialisation="legacy"` to use the legacy PyTorch bin format.
+
 `langchain.llms.OpenLLM` has the capability to interact with remote OpenLLM
 Server. Given there is an OpenLLM server deployed elsewhere, you can connect to
 it by specifying its URL:
@@ -599,6 +603,7 @@ llm = OpenLLM(
     model_name='flan-t5',
     model_id='google/flan-t5-large',
     embedded=False,
+    serialisation="legacy"
 )
 tools = load_tools(["serpapi", "llm-math"], llm=llm)
 agent = initialize_agent(