Fix indentation in leaderboard README (#449)

The code examples in the README use both triple backticks and
indentation, which is not necessary and makes the code examples appear
excessively indented. This is also annoying if you use the bash
`HISTCONTROL=ignorespace` option, which causes commands prefaced with a
space to not appear in history.

This PR removes the extra indentation.

Co-authored-by: Paul O'Leary McCann <[email protected]>

berkeley-function-call-leaderboard/README.md

@@ -17,10 +17,10 @@ Read more about the technical details and interesting insights in our blog post!
 Before generating the leaderboard statistics, you should install dependencies using the following command: 
 
 ```bash
-    conda create -n BFCL python=3.10
-    conda activate BFCL
-    pip install -r requirements.txt # Inside ./berkeley-function-call-leaderboard
-    pip install vllm # If you have vLLM supported GPU(s) and want to run our evaluation data against self-hosted OSS models.
+conda create -n BFCL python=3.10
+conda activate BFCL
+pip install -r requirements.txt # Inside ./berkeley-function-call-leaderboard
+pip install vllm # If you have vLLM supported GPU(s) and want to run our evaluation data against self-hosted OSS models.
 ```
 If you plan to evaluate on OSS models, we are using vLLM for inference and refer to https://github.com/vllm-project/vllm for detail. We recommend to inference on at least V100s, A100s, and latest GPUs that are supported by vLLM. 
 
@@ -47,7 +47,7 @@ ln -s eval_checker/tree-sitter-javascript tree-sitter-javascript
 To download the evaluation dataset from huggingface, from the current directory `./berkeley-function-call-leaderboard`, run the following command:
 
 ```bash
-    huggingface-cli download gorilla-llm/Berkeley-Function-Calling-Leaderboard --local-dir ./data --repo-type dataset
+huggingface-cli download gorilla-llm/Berkeley-Function-Calling-Leaderboard --local-dir ./data --repo-type dataset
 ```
 
 
@@ -80,13 +80,13 @@ To run the executable test categories, there are 4 API keys to fill out:
 The `apply_function_credential_config.py` inputs an input file, optionally an outputs file. If the output file is not given as an argument, it will overwrites your original file with the cleaned data.
 
 ```bash
-    python apply_function_credential_config.py --input-file ./data/gorilla_openfunctions_v1_test_rest.json
+python apply_function_credential_config.py --input-file ./data/gorilla_openfunctions_v1_test_rest.json
 ```
 
 Then, use `eval_data_compilation.py` to compile all files by using
 
 ```bash
-    python eval_data_compilation.py
+python eval_data_compilation.py
 ```
 ## Berkeley Function-Calling Leaderboard Statistics
 
@@ -95,20 +95,20 @@ To run Mistral Models function calling, you need to have `mistralai >= 0.1.3`.
 Also provide your API keys in your environment variables.
 
 ```bash
-    export OPENAI_API_KEY=sk-XXXXXX
-    export MISTRAL_API_KEY=XXXXXX
-    export FIRE_WORKS_API_KEY=XXXXXX
-    export ANTHROPIC_API_KEY=XXXXXX
-    export COHERE_API_KEY=XXXXXX
-    export NVIDIA_API_KEY=nvapi-XXXXXX
+export OPENAI_API_KEY=sk-XXXXXX
+export MISTRAL_API_KEY=XXXXXX
+export FIRE_WORKS_API_KEY=XXXXXX
+export ANTHROPIC_API_KEY=XXXXXX
+export COHERE_API_KEY=XXXXXX
+export NVIDIA_API_KEY=nvapi-XXXXXX
 ```
 
 To generate leaderboard statistics, there are two steps:
 
 1. Inference the evaluation data and obtain the results from specific models 
 
 ```bash
-    python openfunctions_evaluation.py --model MODEL_NAME --test-category TEST_CATEGORY
+python openfunctions_evaluation.py --model MODEL_NAME --test-category TEST_CATEGORY
 ```
 For TEST_CATEGORY, we have `executable_simple`, `executable_parallel_function`, `executable_multiple_function`, `executable_parallel_multiple_function`, `simple`, `relevance`, `parallel_function`, `multiple_function`, `parallel_multiple_function`, `java`, `javascript`, `rest`, `sql`, `chatable`.
 
@@ -128,7 +128,7 @@ If decided to run OSS model, openfunction evaluation uses vllm and therefore req
 Navigate to the `./berkeley-function-call-leaderboard/eval_checker` directory and run the `eval_runner.py` script with the desired parameters. The basic syntax is as follows:
 
 ```bash
-    python ./eval_runner.py --model MODEL_NAME --test-category {TEST_CATEGORY,all,ast,executable,python,non-python}
+python ./eval_runner.py --model MODEL_NAME --test-category {TEST_CATEGORY,all,ast,executable,python,non-python}
 ```
 
 - `MODEL_NAME`: Optional. The name of the model you wish to evaluate. This parameter can accept multiple model names separated by spaces. Eg, `--model gorilla-openfunctions-v2 gpt-4-0125-preview`.
@@ -164,19 +164,19 @@ Navigate to the `./berkeley-function-call-leaderboard/eval_checker` directory an
 If you want to run all tests for the `gorilla-openfunctions-v2` model, you can use the following command:
 
 ```bash
-    python ./eval_runner.py --model gorilla-openfunctions-v2
+python ./eval_runner.py --model gorilla-openfunctions-v2
 ```
 
 If you want to runn `rest` tests for all GPT models, you can use the following command:
 
 ```bash
-    python ./eval_runner.py --model gpt-3.5-turbo-0125 gpt-4-0613 gpt-4-1106-preview gpt-4-0125-preview --test-category rest
+python ./eval_runner.py --model gpt-3.5-turbo-0125 gpt-4-0613 gpt-4-1106-preview gpt-4-0125-preview --test-category rest
 ```
 
 If you want to run `rest` and `javascript` tests for all GPT models and `gorilla-openfunctions-v2`, you can use the following command:
 
 ```bash
-    python ./eval_runner.py --model gorilla-openfunctions-v2 gpt-3.5-turbo-0125 gpt-4-0613 gpt-4-1106-preview gpt-4-0125-preview --test-category rest javascript
+python ./eval_runner.py --model gorilla-openfunctions-v2 gpt-3.5-turbo-0125 gpt-4-0613 gpt-4-1106-preview gpt-4-0125-preview --test-category rest javascript
 ```
 
 ### Model-Specific Optimization