From f748d41e0a49674968618a29ae18e41eb726716b Mon Sep 17 00:00:00 2001
From: Eissa Jamil <eissajamil@gmail.com>
Date: Mon, 4 Mar 2024 13:22:57 -0800
Subject: [PATCH] Update
Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb
quick whitepaper reference added to the INST Prompt Tags section
---
.../Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/examples/examples_with_aws/Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb b/examples/examples_with_aws/Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb
index f873fa96..928a46f5 100644
--- a/examples/examples_with_aws/Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb
+++ b/examples/examples_with_aws/Prompt_Engineering_with_Llama_2_On_Amazon_Bedrock.ipynb
@@ -410,7 +410,9 @@
"**Prompt Format Example:** `[INST] {prompt_1} [/INST]`\n",
"\n",
"#### Why?\n",
- "In theory, you could use the previous section's roles to instruct the model, for example by using `User:` or `Assistant:`, but for longer conversations it's possible the model responses may forget the role and you may need prompt with the roles again, or the model could begin including the roles in the response. By using the `[INST][/INST]` tags, the model may have more consistent and accurate response over the longer conversations, and you will not run the risk of the tags being included in the response.\n",
+ "In theory, you could use the previous section's roles to instruct the model, for example by using `User:` or `Assistant:`, but for longer conversations it's possible the model responses may forget the role and you may need prompt with the roles again, or the model could begin including the roles in the response. By using the `[INST][/INST]` tags, the model may have more consistent and accurate response over the longer conversations, and you will not run the risk of the tags being included in the response. \n",
+ "\n",
+ "You can read more about using [INST] tags in the [Llama 2 Whitepaper](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/), in **3.3 System Message for Multi-Turn Consistency**, where you can read about Ghost Attention (GAtt) and the GAtt method used with Llama 2. \n",
"\n",
"#### Examples:\n",
"`[INST]\n",
@@ -1266,7 +1268,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
--
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