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chore: fixed some typos and attribute issues in README (#2891)

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* chore: fixed html repeated attribute in README

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* chore: fixed spelling mistakes in README
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@ -1,7 +1,7 @@
<div align="center"> <div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0"> <a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png"> <img width=560 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a> </a>
# Text Generation Inference # Text Generation Inference
@ -141,8 +141,8 @@ You have the option to utilize the `HF_TOKEN` environment variable for configuri
For example, if you want to serve the gated Llama V2 model variants: For example, if you want to serve the gated Llama V2 model variants:
1. Go to https://huggingface.co/settings/tokens 1. Go to https://huggingface.co/settings/tokens
2. Copy your cli READ token 2. Copy your CLI READ token
3. Export `HF_TOKEN=<your cli READ token>` 3. Export `HF_TOKEN=<your CLI READ token>`
or with Docker: or with Docker:
@ -157,7 +157,7 @@ docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/da
### A note on Shared Memory (shm) ### A note on Shared Memory (shm)
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by [`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
`PyTorch` to do distributed training/inference. `text-generation-inference` make `PyTorch` to do distributed training/inference. `text-generation-inference` makes
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models. use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
@ -196,7 +196,7 @@ Detailed blogpost by Adyen on TGI inner workings: [LLM inference at scale with T
You can also opt to install `text-generation-inference` locally. You can also opt to install `text-generation-inference` locally.
First clone the repository and change directoy into it: First clone the repository and change directory into it:
```shell ```shell
git clone https://github.com/huggingface/text-generation-inference git clone https://github.com/huggingface/text-generation-inference
@ -213,7 +213,7 @@ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.11 conda create -n text-generation-inference python=3.11
conda activate text-generation-inference conda activate text-generation-inference
#using pyton venv #using python venv
python3 -m venv .venv python3 -m venv .venv
source .venv/bin/activate source .venv/bin/activate
``` ```