doc updates 3

app.py

@@ -30,7 +30,7 @@ with demo:
         follow the instructions and complete the form in the 🏎️ Submit tab. Models submitted to the leaderboard are evaluated 
         on the Intel Developer Cloud ☁️. The evaluation platform consists of Gaudi Accelerators and Xeon CPUs running benchmarks from
         the  [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness).""")
-    gr.Markdown("""![DevHub-image](assets/DevHub_Logo.png) Join  5000+ developers on the [Intel DevHub Discord](https://discord.gg/yNYNxK2k) to get support with your submission and 
+    gr.Markdown("""Join  5000+ developers on the [Intel DevHub Discord](https://discord.gg/yNYNxK2k) to get support with your submission and 
                 talk about everything from GenAI, HPC, to Quantum Computing.""")
     gr.Markdown("""A special shout-out to the 🤗 [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) 
                 team for generously sharing their code and best 

info/deployment.py

@@ -95,9 +95,11 @@ The Intel® Data Center GPU Max Series is Intel's highest performing, highest de
 
 ### INT4 Inference (GPU) with Intel Extension for Transformers and Intel Extension for Python
 Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU.
+
 👍 [Intel Extension for Transformers GitHub](https://github.com/intel/intel-extension-for-transformers)
 
 Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* xpu device.
+
 👍 [Intel Extension for PyTorch GitHub](https://github.com/intel/intel-extension-for-pytorch)
 
 ```python
@@ -125,6 +127,7 @@ The Intel® Xeon® CPUs have the most built-in accelerators of any CPU on the ma
 
 ### Optimum Intel and Intel Extension for PyTorch (no quantization)
 🤗 Optimum Intel is the interface between the 🤗 Transformers and Diffusers libraries and the different tools and libraries provided by Intel to accelerate end-to-end pipelines on Intel architectures.
+
 👍 [Optimum Intel GitHub](https://github.com/huggingface/optimum-intel)
 
 Requires installing/updating optimum `pip install --upgrade-strategy eager optimum[ipex]`
@@ -179,6 +182,7 @@ Intel® Core™ Ultra Processors are optimized for premium thin and powerful lap
 
 ### Intel® NPU Acceleration Library
 The Intel® NPU Acceleration Library is a Python library designed to boost the efficiency of your applications by leveraging the power of the Intel Neural Processing Unit (NPU) to perform high-speed computations on compatible hardware.
+
 👍 [Intel NPU Acceleration Library GitHub](https://github.com/intel/intel-npu-acceleration-library)
 
 ```python
@@ -214,6 +218,7 @@ _ = model.generate(**generation_kwargs)
 
 ### OpenVINO Tooling with Optimum Intel
 OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
+
 👍 [OpenVINO GitHub](https://github.com/openvinotoolkit/openvino)
 
 ```python
@@ -235,12 +240,13 @@ pipe("In the spring, beautiful flowers bloom...")
 # Intel® Gaudi Accelerators
 The Intel Gaudi 2 accelerator is Intel's most capable deep learning chip. You can learn about Gaudi 2 [here](https://habana.ai/products/gaudi2/).
 
-Habana's SDK, Intel Gaudi Software, supports PyTorch and DeepSpeed for accelerating LLM training and inference. 
+Intel Gaudi Software supports PyTorch and DeepSpeed for accelerating LLM training and inference. 
 The Intel Gaudi Software graph compiler will optimize the execution of the operations accumulated in the graph 
 (e.g. operator fusion, data layout management, parallelization, pipelining and memory management, 
 and graph-level optimizations).
 
 Optimum Habana provides covenient functionality for various tasks. Below is a command line snippet to run inference on Gaudi with meta-llama/Llama-2-7b-hf.
+
 👍[Optimum Habana GitHub](https://github.com/huggingface/optimum-habana)
 
 The "run_generation.py" script below can be found [here on GitHub](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation)