Make organization card evergreen

README.md

@@ -4,46 +4,31 @@ sdk: static
 pinned: false
 ---
 
-# Vontra
-
-### Local AI, real hardware, and useful MLX releases for the community.
-
-Hi — I’m one person with a strong interest in local AI.
+<div align="center">
 
-I spend my time trying models on hardware I actually own, working through the awkward compatibility problems, and sharing the useful results. Most of my work here is focused on **MLX and Apple silicon**: model conversions, quantized checkpoints, architecture support, serving notes, and the occasional bit of glue needed to make a model run properly.
+# Vontra
 
-There is no big company or team behind this page. I’m simply exploring what local hardware can do and trying to make that work useful to other people.
+### Exploring local AI and sharing useful MLX work with the community.
 
-## The hardware
+</div>
 
-- Mac Studio
-- MacBook Pro
-- NVIDIA DGX Spark
+Vontra is my personal local-AI project. I’m one person experimenting with open models, learning what today’s hardware can do, and publishing anything that might save somebody else time.
 
-The Apple machines are where most of the MLX work happens. The DGX Spark gives me another local platform for comparison and experimentation.
+My main focus is **MLX and Apple silicon**. I run models on a Mac Studio and MacBook Pro, work through conversion and compatibility problems, and share usable checkpoints, code and notes when I get them running. I also use an NVIDIA DGX Spark for local inference experiments outside the Apple ecosystem.
 
-## What I share
+## What you’ll find here
 
 - MLX conversions and quantized checkpoints
-- Support for models that do not yet work cleanly with stock MLX-LM
-- Practical oMLX and local-serving notes
-- Measured memory and generation results when available
-- Clear attribution to the original model creators
-- Honest caveats when something is experimental
-
-## Current MLX releases
-
-| Model family | Available builds |
-| --- | --- |
-| **DeepSeek V4 Flash** | [MXFP4 MLX](https://huggingface.co/Vontra/DeepSeek-V4-Flash-0731-MXFP4-MLX) |
-| **Ling 3.0 Flash** | [4-bit](https://huggingface.co/Vontra/Ling-3.0-flash-MLX-4bit) · [8-bit](https://huggingface.co/Vontra/Ling-3.0-flash-MLX-8bit) |
-| **Laguna S 2.1** | [4-bit](https://huggingface.co/Vontra/Laguna-S-2.1-MLX-4bit) · [6-bit](https://huggingface.co/Vontra/Laguna-S-2.1-MLX-6bit) · [8-bit](https://huggingface.co/Vontra/Laguna-S-2.1-MLX-8bit) |
-| **Solar Open 2 250B** | [4-bit](https://huggingface.co/Vontra/Solar-Open2-250B-MLX-4bit) · [6-bit](https://huggingface.co/Vontra/Solar-Open2-250B-MLX-6bit) · [8-bit](https://huggingface.co/Vontra/Solar-Open2-250B-MLX-8bit) |
+- Compatibility work for architectures that need more than a stock conversion
+- Practical notes from running and serving models locally
+- Memory and performance observations where they are useful
+- Clear links and credit to the original model creators
+- Honest warnings when something is experimental or incomplete
 
-## Why I publish this work
+## Why I share it
 
-Local AI gets better when people share what worked, what failed, and what had to be changed along the way. If one of these releases saves someone else a long conversion, a difficult compatibility fix, or a few hours of testing, then publishing it was worthwhile.
+Local AI moves quickly, and a lot of the difficult work happens between a model being released and somebody being able to run it well on their own machine. Sharing that work makes the ecosystem more accessible and helps the next person avoid solving the same problems again.
 
-All base models belong to their original authors and are released under their respective licences. Please check each model card for upstream attribution, usage instructions, trust requirements, and limitations.
+This is a personal, best-effort project rather than a company or research lab. I don’t train the base models, and every release remains the work of its original authors. Each repository should explain what was changed, how it was tested, what needs to be trusted, and which upstream licence applies.
 
-If you find a problem, open a discussion on the relevant model repository. Useful reports and reproducible results are always welcome.
+If something here helps, use it. If something breaks, a clear and reproducible report is always appreciated.