Rebrand model card to TensorFold

README.md

@@ -18,6 +18,11 @@ tags:
   - 4-bit
 ---
 
+<p align="center">
+  <a href="https://tensorfold.dev">
+    <img src="https://huggingface.co/spaces/TensorFold/README/resolve/main/tensorfold-logo.png" alt="TensorFold" width="160">
+  </a>
+</p>
 
 <p align="center">
   <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" width="92" alt="Hugging Face logo">
@@ -27,7 +32,7 @@ tags:
 <p align="center">
   <img src="https://img.shields.io/badge/Meta-Muse_Glimmer-0467DF?style=for-the-badge&logo=meta&logoColor=white" alt="Meta Muse Glimmer">
   <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
-  <img src="https://img.shields.io/badge/Vontra-MLX_VLM-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra MLX VLM">
+  <img src="https://img.shields.io/badge/TensorFold-MLX_VLM-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="TensorFold MLX VLM">
 </p>
 
 
@@ -43,7 +48,7 @@ tags:
   <a href="https://huggingface.co/meta-models/Muse-Glimmer-30B">Original model</a> ·
   <a href="https://github.com/Blaizzy/mlx-vlm">MLX-VLM</a> ·
   <a href="https://github.com/ml-explore/mlx">MLX</a> ·
-  <a href="https://huggingface.co/Vontra">More Vontra conversions</a>
+  <a href="https://huggingface.co/TensorFold">More TensorFold conversions</a>
 </p>
 
 
@@ -121,8 +126,8 @@ Download the checkpoint if a local copy is preferred:
 
 
 ```bash
-hf download Vontra/Muse-Glimmer-30B-MLX-4bit \
-  --local-dir ~/.omlx/models/Vontra/Muse-Glimmer-30B-MLX-4bit
+hf download TensorFold/Muse-Glimmer-30B-MLX-4bit \
+  --local-dir ~/.omlx/models/TensorFold/Muse-Glimmer-30B-MLX-4bit
 ```
 
 
@@ -133,7 +138,7 @@ hf download Vontra/Muse-Glimmer-30B-MLX-4bit \
 from mlx_vlm import generate, load
 
 
-model, processor = load("Vontra/Muse-Glimmer-30B-MLX-4bit")
+model, processor = load("TensorFold/Muse-Glimmer-30B-MLX-4bit")
 tokenizer = processor.tokenizer if hasattr(processor, "tokenizer") else processor
 messages = [
     {"role": "user", "content": "Explain unified memory on Apple silicon."}
@@ -168,7 +173,7 @@ The multimodal path was smoke-tested locally with a real image. Include an image
 from mlx_vlm import generate, load
 
 
-model, processor = load("Vontra/Muse-Glimmer-30B-MLX-4bit")
+model, processor = load("TensorFold/Muse-Glimmer-30B-MLX-4bit")
 tokenizer = processor.tokenizer if hasattr(processor, "tokenizer") else processor
 messages = [
     {
@@ -241,14 +246,14 @@ This is a community conversion, not an official Meta release. Validate quality,
 The upstream model is released under the **Apache License 2.0**. The upstream `LICENSE` and `USAGE_POLICY.md` files are included in this repository; use is subject to both the licence and the upstream usage policy.
 
 
-All model design, training, benchmark, and upstream documentation credit belongs to Meta and the original contributors. The MLX conversion, Apple-silicon validation, compatibility work, and model card are provided by [Vontra](https://huggingface.co/Vontra).
+All model design, training, benchmark, and upstream documentation credit belongs to Meta and the original contributors. The MLX conversion, Apple-silicon validation, compatibility work, and model card are provided by [TensorFold](https://huggingface.co/TensorFold).
 
 
 
-<!-- vontra-chooser-start -->
+<!-- TensorFold-chooser-start -->
 ## Choose for your Mac
 
-[64GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-64gb-macs-6a9fefda17932216ec9ab457) · [128GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-128gb-macs-6a9ff0abd31bc9abbe7922d7) · [256GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-256gb-macs-6a9ff0ef9fed7c5bdca15e9b)
+[64GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-64gb-macs-6a9fefda17932216ec9ab457) · [128GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-128gb-macs-6a9ff0abd31bc9abbe7922d7) · [256GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-256gb-macs-6a9ff0ef9fed7c5bdca15e9b)
 
 Published peak memory: **21.33 GB**; estimated starting tier: **64GB**, leaving about **42 GB** nominal headroom. The collections use published M3 Studio peaks with at least 25% nominal headroom; fit on other Macs is an estimate, and full context is not guaranteed. Start with short context and one request.
 
@@ -259,7 +264,7 @@ The exact tested oMLX application version is not recorded here; a library versio
 ### Quick start and demo prompt
 
 ```bash
-hf download Vontra/Muse-Glimmer-30B-MLX-4bit --local-dir ./models/Muse-Glimmer-30B-MLX-4bit
+hf download TensorFold/Muse-Glimmer-30B-MLX-4bit --local-dir ./models/Muse-Glimmer-30B-MLX-4bit
 ```
 
 Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.
@@ -272,5 +277,5 @@ Explain why the sky looks blue in three short sentences.
 
 This is a demo prompt to try, not a recorded successful run; a captured demonstration for this documentation update is not yet available.
 
-[Follow Vontra for new Apple Silicon releases and fixes.](https://huggingface.co/Vontra)
-<!-- vontra-chooser-end -->
+[Follow TensorFold for new Apple Silicon releases and fixes.](https://huggingface.co/TensorFold)
+<!-- TensorFold-chooser-end -->