incorporated review comments

Author: aarora79

recipes/benchmarks/fmbench/README.md

@@ -44,6 +44,10 @@ The report also includes latency Vs prompt size charts for different concurrency
 
 The following steps provide a [Quick start guide for `FMBench`](https://github.com/aws-samples/foundation-model-benchmarking-tool#quickstart). For a more detailed DIY version, please see the [`FMBench Readme`](https://github.com/aws-samples/foundation-model-benchmarking-tool?tab=readme-ov-file#the-diy-version-with-gory-details).
 
+1. Each `FMBench` run works with a configuration file that contains the information about the model, the deployment steps, and the tests to run. A typical `FMBench` workflow involves either directly using an already provided config file from the [`configs`](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main/src/fmbench/configs) folder in the `FMBench` GitHub repo or editing an already provided config file as per your own requirements (say you want to try benchmarking on a different instance type, or a different inference container etc.).
+
+    >A simple config file with key parameters annotated is included in this repo, see [`config.yml`](./config.yml). This file benchmarks performance of Llama2-7b on an `ml.g5.xlarge` instance and an `ml.g5.2xlarge` instance. You can use this provided config file as it is for this Quickstart.
+
 1. Launch the AWS CloudFormation template included in this repository using one of the buttons from the table below. The CloudFormation template creates the following resources within your AWS account: Amazon S3 buckets, Amazon IAM role and an Amazon SageMaker Notebook with this repository cloned. A read S3 bucket is created which contains all the files (configuration files, datasets) required to run `FMBench` and a write S3 bucket is created which will hold the metrics and reports generated by `FMBench`. The CloudFormation stack takes about 5-minutes to create.
 
    |AWS Region                |     Link        |
@@ -65,7 +69,7 @@ The following steps provide a [Quick start guide for `FMBench`](https://github.c
 
     1. We benchmark performance for the `Llama2-7b` model on a `ml.g5.xlarge` and a `ml.g5.2xlarge` instance type, using the `huggingface-pytorch-tgi-inference` inference container. This test would take about 30 minutes to complete and cost about $0.20.
 
-    1. It uses a simple relationship of 750 words equals 1000 tokens, to get a more accurate representation of token counts use the `Llama2 tokenizer` (instructions are provided in the next section). ***It is strongly recommended that for more accurate results on token throughput you use a tokenizer specific to the model you are testing rather than the default tokenizer. See instructions provided later in this document on how to use a custom tokenizer***.
+    1. It uses a simple relationship that 750 words equals 1000 tokens, to get a more accurate representation of token counts use the `Llama2 tokenizer`. **_It is strongly recommended that for more accurate results on token throughput you use a tokenizer specific to the model you are testing rather than the default tokenizer. See instructions provided [here](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main?tab=readme-ov-file#the-diy-version-with-gory-details) on how to use a custom tokenizer_**.
 
         ```{.bash}
         account=`aws sts get-caller-identity | jq .Account | tr -d '"'`
@@ -81,18 +85,18 @@ The following steps provide a [Quick start guide for `FMBench`](https://github.c
 
 1. The generated reports and metrics are available in the `sagemaker-fmbench-write-<replace_w_your_aws_region>-<replace_w_your_aws_account_id>` bucket. The metrics and report files are also downloaded locally and in the `results` directory (created by `FMBench`) and the benchmarking report is available as a markdown file called `report.md` in the `results` directory. You can view the rendered Markdown report in the SageMaker notebook itself or download the metrics and report files to your machine for offline analysis.
 
-## The `config.yml` file
+## 🚨 Benchmarking Llama3 on Amazon Bedrock 🚨
 
-Each `FMBench` run works with a configuration file that contains the information about the model, the deployment steps, and the tests to run. A typical `FMBench` workflow involves either directly using an already provided config file from the [`configs`](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main/src/fmbench/configs) folder in the `FMBench` GitHub repo or editing an already provided config file as per your own requirements (say you want to try benchmarking on a different instance type, or a different inference container etc.).
+Llama3 is now available on Bedrock (read [blog post](https://aws.amazon.com/blogs/aws/metas-llama-3-models-are-now-available-in-amazon-bedrock/)), and you can now benchmark it using `FMBench`. Here is the config file for benchmarking `Llama3-8b-instruct` and `Llama3-70b-instruct` on Bedrock.
 
-A simple config file with key parameters annotated is includes in this repo, see [`config.yml`](./config.yml). This file benchmarks performance of Llama2-7b on an `ml.g5.xlarge` instance and an `ml.g5.2xlarge` instance.
+- [Config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-bedrock-llama3.yml) for `Llama3-8b-instruct` and `Llama3-70b-instruct`.
 
 ## 🚨 Benchmarking Llama3 on Amazon SageMaker 🚨
 
 Llama3 is now available on SageMaker (read [blog post](https://aws.amazon.com/blogs/machine-learning/meta-llama-3-models-are-now-available-in-amazon-sagemaker-jumpstart/)), and you can now benchmark it using `FMBench`. Here are the config files for benchmarking `Llama3-8b-instruct` and `Llama3-70b-instruct` on `ml.p4d.24xlarge` and `ml.g5.12xlarge` instance.
 
-- [Config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-llama3-8b-instruct-g5-p4d.yml) for `Llama3-8b-instruct` on  `ml.p4d.24xlarge` and `ml.g5.12xlarge`
-- [Config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-llama3-70b-instruct-g5-p4d.yml) for `Llama3-70b-instruct` on  `ml.p4d.24xlarge` and `ml.g5.48xlarge`
+- [Config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-llama3-8b-instruct-g5-p4d.yml) for `Llama3-8b-instruct` on  `ml.p4d.24xlarge` and `ml.g5.12xlarge`.
+- [Config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-llama3-70b-instruct-g5-p4d.yml) for `Llama3-70b-instruct` on  `ml.p4d.24xlarge` and `ml.g5.48xlarge`.
 
 ## Benchmarking Llama2 on Amazon SageMaker