address comments

Author: ziqifan617

Feature_Guide/Speculative_Decoding/README.md

@@ -54,6 +54,6 @@ may prove simpler than generating a summary for an article. [Spec-Bench](https:/
 shows the performance of different speculative decoding approaches on different tasks.
 
 ## Speculative Decoding with Triton Inference Server
- Triton Inference Server supports speculative decoding on different types of Triton backend. See what a Triton backend is [here](https://github.com/triton-inference-server/tensorrtllm_backend).
+ Triton Inference Server supports speculative decoding on different types of Triton backends. See what a Triton backend is [here](https://github.com/triton-inference-server/tensorrtllm_backend).
 - Follow [here](TRT-LLM/README.md) to learn how Triton Inference Server supports speculative decoding with [TensorRT-LLM Backend](https://github.com/triton-inference-server/tensorrtllm_backend).
 - Follow [here](vLLM/README.md) to learn how Triton Inference Server supports speculative decoding with [vLLM Backend](https://github.com/triton-inference-server/vllm_backend).

Feature_Guide/Speculative_Decoding/TRT-LLM/README.md

@@ -49,7 +49,7 @@ EAGLE ([paper](https://arxiv.org/pdf/2401.15077) | [github](https://github.com/S
 ### Acquiring EAGLE Model and its Base Model
 
 In this example, we will be using the [EAGLE-Vicuna-7B-v1.3](https://huggingface.co/yuhuili/EAGLE-Vicuna-7B-v1.3) model.
-More types of EAGLE models could be found [here](https://huggingface.co/yuhuili). The base model [Vicuna-7B-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3) is also needed for EAGLE to work.
+More types of EAGLE models can be found [here](https://huggingface.co/yuhuili). The base model [Vicuna-7B-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3) is also needed for EAGLE to work.
 
 To download both models, run the following command:
 ```bash
@@ -66,7 +66,7 @@ Launch Triton docker container with TensorRT-LLM backend.
 Note that we're mounting the downloaded EAGLE and base models to `/hf-models` in the docker container.
 Make an `engines` folder outside docker to reuse engines for future runs.
 Please, make sure to replace <xx.yy> with the version of Triton that you want
-to use (must be >= 25.01). The latest Triton Server container is recommended and could be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver/tags).
+to use (must be >= 25.01). The latest Triton Server container is recommended and can be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver/tags).
 
 ```bash
 docker run --rm -it --net host --shm-size=2g \
@@ -226,7 +226,7 @@ format required by Gen-AI Perf. Note that MT-bench could not be used since Gen-A
 ```bash
 wget https://raw.githubusercontent.com/SafeAILab/EAGLE/main/eagle/data/humaneval/question.jsonl
 
-# dataset-converter.py file can be found in the parent folder as this README.
+# dataset-converter.py file can be found in the parent folder of this README.
 python3 dataset-converter.py --input_file question.jsonl --output_file converted_humaneval.jsonl
 ```
 

Feature_Guide/Speculative_Decoding/vLLM/README.md

@@ -45,7 +45,7 @@ EAGLE ([paper](https://arxiv.org/pdf/2401.15077) | [github](https://github.com/S
 ### Acquiring EAGLE Model and its Base Model
 
 In this example, we will be using the [EAGLE-LLaMA3-Instruct-8B](https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B) model.
-More types of EAGLE models could be found [here](https://huggingface.co/yuhuili). The base model [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) is also needed for EAGLE to work.
+More types of EAGLE models can be found [here](https://huggingface.co/yuhuili). The base model [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) is also needed for EAGLE to work.
 
 To download both models, run the following command:
 ```bash