readme updates

Author: aarora79

README.md

@@ -149,7 +149,6 @@ Llama3 is now available on SageMaker (read [blog post](https://aws.amazon.com/bl
 1. Don't delete endpoints for the bring your own endpoint case.
 1. Fix bug with business summary chart.
 
-
 ### v1.0.32
 
 1. Report enhancements: New business summary chart, config file embedded in the report, version numbering and others.
@@ -305,27 +304,10 @@ For some enterprise scenarios it might be desirable to run `FMBench` directly on
 
 1. All metrics are stored in the `/tmp/fmbench-write` directory created automatically by the `fmbench` package. Once the run completes all files are copied locally in a `results-*` folder as usual.
 
-### Steps to run
-
-1. `pip install` the `FMBench` package from PyPi.
-
-1. Create a config file using one of the config files available [here](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main/src/fmbench/configs).
-    1. The configuration file is a YAML file containing configuration for all steps of the benchmarking process. It is recommended to create a copy of an existing config file and tweak it as necessary to create a new one for your experiment.
-
-1. Create the read and write buckets as mentioned in the prerequisites section. Mention the respective directories for your read and write buckets within the config files.
-
-1. Run the `FMBench` tool from the command line.
-
-    ```{.bash}
-    # the config file path could be an S3 path and https path 
-    # or even a path to a file on the local filesystem
-    fmbench --config-file /path/to/config/file
-    ```
-
-1. Depending upon the experiments in the config file, the `FMBench` run may take a few minutes to several hours. Once the run completes, you can find the report and metrics in the write S3 bucket set in the [config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-mistral-7b-tgi-g5.yml#L12). The report is generated as a markdown file called `report.md` and is available in the metrics directory in the write S3 bucket.
-
 ## Results
 
+Depending upon the experiments in the config file, the `FMBench` run may take a few minutes to several hours. Once the run completes, you can find the report and metrics in the local `results-*` folder in the directory from where `FMBench` was run. The rpeort and metrics are also written to the write S3 bucket set in the [config file](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/config-mistral-7b-tgi-g5.yml#L12).
+
 Here is a screenshot of the `report.md` file generated by `FMBench`.
 ![Report](https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/img/results.gif?raw=true)
 
@@ -368,15 +350,15 @@ You can create an internal `FMBench` website to view results from multiple runs
 Choose any config file from the [`bedrock`](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main/src/fmbench/configs/bedrock) folder and either run these directly or use them as templates for creating new config files specific to your use-case. Here is an example for benchmarking the `Llama3` models on Bedrock.
 
 ```{.bash}
-fmbench --config-file https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/bedrock/config-bedrock-llama3.yml > fmbench.log 2>&1
+fmbench --config-file https://raw.githubusercontent.com/aws-samples/foundation-model-benchmarking-tool/main/src/fmbench/configs/bedrock/config-bedrock-llama3.yml > fmbench.log 2>&1
 ```
 
 ### Benchmark models on SageMaker
 
 Choose any config file from the model specific folders, for example the [`Llama3`](https://github.com/aws-samples/foundation-model-benchmarking-tool/tree/main/src/fmbench/configs/llama3) folder for `Llama3` family of models. These configuration files also include instructions for `FMBench` to first deploy the model on SageMaker using your configured instance type and inference parameters of choice and then run the benchmarking. Here is an example for benchmarking `Llama3-8b` model on an `ml.inf2.24xlarge` and `ml.g5.12xlarge` instance. 
 
 ```{.bash}
-fmbench --config-file https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/llama3/8b/config-llama3-8b-inf2-g5.yml > fmbench.log 2>&1
+fmbench --config-file https://raw.githubusercontent.com/aws-samples/foundation-model-benchmarking-tool/main/src/fmbench/configs/llama3/8b/config-llama3-8b-inf2-g5.yml > fmbench.log 2>&1
 ```
 
 ### Benchmark models on EKS
@@ -408,7 +390,7 @@ The steps for deploying the model on your EKS cluster are described below.
 1. Run the `Llama3-8b` benchmarking using the command below (replace the config file as needed for a different model). This will first deploy the model on your EKS cluster and then run benchmarking on the deployed model.
 
     ```{.bash}
-    fmbench --config-file https://github.com/aws-samples/foundation-model-benchmarking-tool/blob/main/src/fmbench/configs/llama3/8b/config-llama3-8b-eks-inf2.yml > fmbench.log 2>&1
+    fmbench --config-file https://raw.githubusercontent.com/aws-samples/foundation-model-benchmarking-tool/main/src/fmbench/configs/llama3/8b/config-llama3-8b-eks-inf2.yml > fmbench.log 2>&1
     ```
 
 1. As the model is getting deployed you might want to run the following `kubectl` commands to monitor the deployment progress. Set the _model_namespace_ to `llama3` or `mistral` or a different model as appropriate.