fix(review): add suggested changes

Author: baptistecolle

intel-gaudi-backend-for-tgi.md

@@ -11,7 +11,8 @@ We're excited to announce the native integration of Intel Gaudi hardware support
 
 ## ✨ What's New? 
 
-We've fully integrated Gaudi support into TGI's main codebase. Previously, we maintained a separate fork for Gaudi devices at [tgi-gaudi](https://github.com/huggingface/tgi-gaudi). This was cumbersome for users and prevented us from supporting the latest TGI features at launch. Now using the new [TGI multi-backend architecture](https://huggingface.co/blog/tgi-multi-backend), we support Gaudi directly on TGI no more finicking on a custom repository 🙌
+We've fully integrated Gaudi support into TGI's main codebase in PR [#3091](https://github.com/huggingface/text-generation-inference/pull/3091). Previously, we maintained a separate fork for Gaudi devices at [tgi-gaudi](https://github.com/huggingface/tgi-gaudi). This was cumbersome for users and prevented us from supporting the latest TGI features at launch. Now using the new [TGI multi-backend architecture](https://huggingface.co/blog/tgi-multi-backend), we support Gaudi directly on TGI – no more finicking on a custom repository 🙌
+
 This integration supports Intel's full line of Gaudi hardware:
 - Gaudi1 💻: Available on [AWS EC2 DL1 instances](https://aws.amazon.com/ec2/instance-types/dl1/)
 - Gaudi2 💻💻: Available on [Intel Dev Cloud](https://ai.cloud.intel.com/)
@@ -34,11 +35,13 @@ The easiest way to run TGI on Gaudi is to use our official Docker image. You nee
 model=meta-llama/Meta-Llama-3.1-8B-Instruct 
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run 
 hf_token=YOUR_HF_ACCESS_TOKEN
-docker run --runtime=habana --cap-add=sys_nice --ipc=host
- -p 8080:80 -v $volume:/data 
--e HF_TOKEN=$hf_token 
--e HABANA_VISIBLE_DEVICES=all
-ghcr.io/huggingface/text-generation-inference:3.2.1-gaudi
+
+docker run --runtime=habana --cap-add=sys_nice --ipc=host \
+ -p 8080:80 \
+ -v $volume:/data \
+ -e HF_TOKEN=$hf_token \
+ -e HABANA_VISIBLE_DEVICES=all \
+ ghcr.io/huggingface/text-generation-inference:3.2.1-gaudi \
  --model-id $model 
 ```
 
@@ -51,6 +54,8 @@ curl 127.0.0.1:8080/generate
  -H 'Content-Type: application/json'
 ```
 
+For comprehensive documentation on using TGI with Gaudi, including how-to guides and advanced configurations, refer to the new dedicated [Gaudi backend documentation](https://huggingface.co/docs/text-generation-inference/backends/gaudi).
+
 ## 🎉 Top features
 
 We have optimized the following models for both single and multi-card configurations. This means these models run as fast as possible on Intel Gaudi. We've specifically optimized the modeling code to target Intel Gaudi hardware, ensuring we offer the best performance and fully utilize Gaudi's capabilities:
@@ -65,10 +70,10 @@ We have optimized the following models for both single and multi-card configurat
 - Gemma (7B) 
 - Llava-v1.6-Mistral-7B 
 
-Furthermore, we also support all models implemented in Transformers library, providing a [fallback mechanism](https://huggingface.co/docs/text-generation-inference/basic_tutorials/non_core_models) that ensures you can still run any model on Gaudi hardware even if it's not yet specifically optimized.
+Furthermore, we also support all models implemented in the [Transformers library](https://huggingface.co/docs/transformers/index), providing a [fallback mechanism](https://huggingface.co/docs/text-generation-inference/basic_tutorials/non_core_models) that ensures you can still run any model on Gaudi hardware even if it's not yet specifically optimized.
 
 🏃‍♂️ We also offer many advanced features on Gaudi hardware, such as FP8 quantization thanks to [Intel Neural Compressor (INC)](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html), enabling even greater performance optimizations.
 
 ## 💪 Getting Involved 
 
-We invite the community to try out TGI on Gaudi hardware and provide feedback. The full documentation is available in the TGI repository. 📚 If you're interested in contributing, check out our contribution guidelines or open an issue with your feedback on GitHub. 🤝 By bringing Intel Gaudi support directly into TGI, we're continuing our mission to provide flexible, efficient, and production-ready tools for deploying LLMs. We're excited to see what you'll build with this new capability! 🎉
+We invite the community to try out TGI on Gaudi hardware and provide feedback. The full documentation is available in the [TGI Gaudi backend documentation](https://huggingface.co/docs/text-generation-inference/backends/gaudi). 📚 If you're interested in contributing, check out our contribution guidelines or open an issue with your feedback on GitHub. 🤝 By bringing Intel Gaudi support directly into TGI, we're continuing our mission to provide flexible, efficient, and production-ready tools for deploying LLMs. We're excited to see what you'll build with this new capability! 🎉