From 315102b03bf18a1e2a130a6beb55fc7db6ad90fd Mon Sep 17 00:00:00 2001
From: Bo Pan <1450662430@qq.com>
Date: Sun, 7 Apr 2024 22:49:32 +0800
Subject: [PATCH] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index a275490..a0c2b45 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ git clone https://github.com/AgentCoord/AgentCoord.git
 cd AgentCoord
 ```
 
-Step 2: Config LLM (see [LLM configuration (use docker)](README.md#llm-configuration-if-use-docker)): 
+Step 2: Config LLM (see [LLM configuration (use docker)](README.md#llm-configuration-use-docker)): 
 
 Step 3: Start the servers
 ```bash
@@ -50,7 +50,7 @@ Step 4: Open http://localhost:8080/ to use the system.
 You can set the configuration (i.e. API base, API key, Model name) for default LLM in ./docker-compose.yml. Currently, we only support OpenAI’s LLMs as the default model. We recommend using gpt-4-turbo-preview as the default model (WARNING: the execution process of multiple agents may consume a significant number of tokens). You can switch to a fast mode that uses the Mistral 8×7B model with hardware acceleration by [Groq](https://groq.com/) for the first time in strategy generation to strike a balance of response quality and efficiency. To achieve this, you need to set the FAST_DESIGN_MODE field in the yaml file as True and fill the GROQ_API_KEY field with the api key of [Groq](https://wow.groq.com/).
 
 ### LLM configuration (install on your machine)
-You can set the configuration in ./backend/config/config.yaml. See [LLM configuration (use docker)](#llm-configuration-if-use-docker) for configuration explanations.
+You can set the configuration in ./backend/config/config.yaml. See [LLM configuration (use docker)](#llm-configuration-use-docker) for configuration explanations.
 
 ### Agent configuration
 Currently, we support config agents by [role-prompting](https://arxiv.org/abs/2305.14688). You can customize your agents by changing the role prompts in AgentRepo\agentBoard_v1.json. We plan to support more methods to customize agents (e.g., supporting RAG, or providing a unified wrapper for customized agents) in the future.