Update README.md

README.md

@@ -123,11 +123,18 @@ library_name: vllm
 
 # Model Card for Pixtral-Large-Instruct-2411
 
-Pixtral-Large-Instruct-2411 is an advanced dense Multimodal Large Language Model (LLM) of 124B parameters with state-of-the-art reasoning, knowledge and coding capabilities extending [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407) with Vision and better System Prompt.
+Pixtral-Large-Instruct-2411 is a 124B multimodal model built on top of Mistral Large 2. Pixtral Large is the second model in our multimodal family and demonstrates frontier-level image understanding. Particularly, the model is able to understand documents, charts and natural images, while maintaining the leading text-only understanding of Mistral Large 2.
 
 For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-large-2411/) and [Pixtral 12B blog post](https://mistral.ai/news/pixtral-12b/).
 
 ## Key features
+- Frontier-class multimodal performance
+- State-of-the-art on MathVista
+- Extends Mistral Large 2 without compromising text performance
+- 123B multimodal decoder, 1B parameter vision encoder
+- 128K context window: fits minimum of 30 high-resolution images
+- 
+<!-- 
 - **Multi-lingual by design:** Dozens of languages supported, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch and Polish.
 - **Proficient in coding:** Trained on 80+ coding languages such as Python, Java, C, C++, Javacsript, and Bash. Also trained on more specific languages such as Swift and Fortran.
 - **Agentic-centric:** Best-in-class agentic capabilities with native function calling and JSON outputting.
@@ -136,7 +143,8 @@ For more details about this model please refer to our release [blog post](https:
 - **Large Context:** A large 128k context window.
 - **System Prompt:** Maintains strong adherence and support for more reliable system prompts.
 - **Vision:** A 1B parameter Vision Encoder achieving SOTA vision capabilities.
-
+ -->
+ 
 ### System Prompt
 We appreciate the feedback received from our community regarding our system prompt handling.  
 In response, we have implemented stronger support for system prompts.  
@@ -266,6 +274,7 @@ curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
   }'
 ```
 
+<!-- 
 ### Mistral-Inference
 
 We recommend using [mistral-inference](https://github.com/mistralai/mistral-inference) to quickly try out / "vibe-check" Pixtral-Large-Instruct-2411.
@@ -343,7 +352,7 @@ result = tokenizer.decode(out_tokens[0])
 
 print(result)
 ```
-
+ -->
 ## The Mistral AI Team
 
 Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall