Rebrand model card to TensorFold

README.md

@@ -26,6 +26,11 @@ tags:
   - apple-silicon
 ---
 
+<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>
 
 <div align="center">
   <a href="https://huggingface.co/zai-org/GLM-5.3-Flash">
@@ -38,7 +43,7 @@ tags:
   <img src="https://img.shields.io/badge/Z.ai-GLM--5.3--Flash-111827?style=for-the-badge" alt="Z.ai GLM-5.3-Flash">
   <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/Native_MTP-Included-22C55E?style=for-the-badge" alt="Native MTP included">
-  <img src="https://img.shields.io/badge/Vontra-oQ-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra oQ">
+  <img src="https://img.shields.io/badge/TensorFold-oQ-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="TensorFold oQ">
 </p>
 
 
@@ -65,7 +70,7 @@ tags:
 | Item | Value |
 | --- | --- |
 | Base model | [`zai-org/GLM-5.3-Flash`](https://huggingface.co/zai-org/GLM-5.3-Flash) |
-| Repository | `Vontra/GLM-5.3-Flash-MLX-oQ4-MTP` |
+| Repository | `TensorFold/GLM-5.3-Flash-MLX-oQ4-MTP` |
 | Format | MLX safetensors |
 | Quantisation | oQ4 mixed precision: 4-bit affine base with 554 sensitivity-selected 5/6/8-bit overrides |
 | Base group size | 64 |
@@ -97,7 +102,7 @@ The upstream tokenizer, chat template, multimodal processor, generation configur
 | Vision encoder and projector | source-compatible precision |
 | Native MTP prediction layer | 4-bit affine base with 12 native-MTP overrides at 5/6/8-bit |
 | Other non-quantisable tensors | Preserved at source-compatible precision |
-| Converter | Vontra streamed oQ converter using MLX 0.32.0 |
+| Converter | TensorFold streamed oQ converter using MLX 0.32.0 |
 
 
 Sensitivity was measured with the built-in multilingual code calibration set, 128 samples at 256 tokens. The allocation rule was byte-budgeted layer-sensitivity ranking under the oQ4 target and hard cap. These details are part of the release recipe and should be used when comparing oQ variants.
@@ -128,7 +133,7 @@ GLM-5.3-Flash uses the new `glm5_next` multimodal architecture, hybrid linear an
 
 
 ```bash
-hf download Vontra/GLM-5.3-Flash-MLX-oQ4-MTP \
+hf download TensorFold/GLM-5.3-Flash-MLX-oQ4-MTP \
   --local-dir GLM-5.3-Flash-MLX-oQ4-MTP
 ```
 
@@ -236,7 +241,7 @@ This is a community quantisation and is not an official Z.ai release.
 The upstream model is released under the **MIT License**. The required licence text is included in this repository.
 
 
-Model design, training, upstream evaluations, and documentation belong to Z.ai and the GLM-5 contributors. The oQ conversion, Apple-silicon validation, native-MTP integration work, and packaging are provided by [Vontra](https://huggingface.co/Vontra).
+Model design, training, upstream evaluations, and documentation belong to Z.ai and the GLM-5 contributors. The oQ conversion, Apple-silicon validation, native-MTP integration work, and packaging are provided by [TensorFold](https://huggingface.co/TensorFold).
 
 
 If you use this model in research, cite the upstream report:
@@ -256,10 +261,10 @@ If you use this model in research, cite the upstream report:
 
 
 
-<!-- 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)
 
 No measured memory tier is assigned here. 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.
 
@@ -270,7 +275,7 @@ oMLX version mentioned in the existing card: **0.6.3rc3**; consult its compatibi
 ### Quick start and demo prompt
 
 ```bash
-hf download Vontra/GLM-5.3-Flash-MLX-oQ4-MTP --local-dir ./models/GLM-5.3-Flash-MLX-oQ4-MTP
+hf download TensorFold/GLM-5.3-Flash-MLX-oQ4-MTP --local-dir ./models/GLM-5.3-Flash-MLX-oQ4-MTP
 ```
 
 Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.
@@ -283,5 +288,5 @@ 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 -->