Release acceptance fixes: desktop commit-memory reserve, bundled edge0-infer, model docs

README.md

@@ -12,6 +12,8 @@
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--8B--A1B--preview-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-8B-A1B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--35B--A3B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--8B--A1B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--145B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-145B-q3k)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--120B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-120B-q3k)
 [![arXiv](https://img.shields.io/badge/arXiv-2609.18063-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2609.18063)
 [![GitHub](https://img.shields.io/badge/GitHub-Edge0--AI%2FEdge0-black?style=for-the-badge&logo=github)](https://github.com/Edge0-AI/Edge0)
 [![Discord](https://img.shields.io/badge/Discord-Join%20us-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://edge0.ai/discord)
@@ -23,7 +25,7 @@ English | [中文](README_zh.md) | [日本語](README_ja.md) | [Español](README
 
 ## News
 
-- **[2026-11-13]** We released **`edge0-145b`** and **`edge0-120b`**, latent mixture-of-experts models derived from Qwen3.8-Flash-Next for a single NVIDIA GPU on Linux or Windows. They run on [edge0-strata](edge0-strata/EDGE0.md), which `edge0-infer serve` and `edge0-infer chat` start for them; see the [model page](docs/models/edge0-145b.md).
+- **[2026-11-13]** We released **`edge0-145b`** and **`edge0-120b`**, latent mixture-of-experts models derived from Qwen3.8-Flash-Next for a single NVIDIA GPU on Linux or Windows. They run on [edge0-strata](edge0-strata/EDGE0.md), which `edge0-infer serve` and `edge0-infer chat` start for them; see the [model page](docs/models/edge0-145b.md). Weights on Hugging Face: [`Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [`Edge0-145B-NVFP4`](https://huggingface.co/Edge0/Edge0-145B-NVFP4) · [`Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [`Edge0-120B-NVFP4`](https://huggingface.co/Edge0/Edge0-120B-NVFP4).
 - **[2026-11-04]** We released the **unified inference engine [`edge0-infer`](edge0-infer/README.md)**: built on [GGML](https://github.com/ggml-org/ggml), one engine and one native C++ API run the same model on macOS, iOS, Android and Windows, with a CLI, an OpenAI-compatible local server, and chat app examples for desktop, Android and iOS. The first release supports `edge0-35b`; `edge0-8b` follows in a later release. It replaces the Python framework and the per-platform engines; the previous code is kept at tag [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer).
 - **[2026-09-30]** We released the **edge0 inference engines for four platforms — iOS, macOS, Android and Windows** (source at tag [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer)).
 - **[2026-09-16]** Our technical report is on arXiv: [The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction](https://arxiv.org/abs/2609.18063).
@@ -75,8 +77,8 @@ trained prerouter heads work together as one unit.
 |---|---|---|---|
 | `edge0-35b` | [`Edge0/Edge0-35B-A3B-preview`](https://huggingface.co/Edge0/Edge0-35B-A3B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview) | 4-bit, 40 layers, 256 experts, prerouter K=4 | Native engine ✅ |
 | `edge0-8b` | [`Edge0/Edge0-8B-A1B-preview`](https://huggingface.co/Edge0/Edge0-8B-A1B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview) | 4-bit, 24 layers, 128 experts, prerouter K=8 | Native engine (planned) |
-| [`edge0-145b`](docs/models/edge0-145b.md) | coming soon | Q3_K or NVFP4 latent experts (1792), 48 layers, 512 experts, K=10 | edge0-strata ✅ (Linux or Windows, NVIDIA GPU) |
-| [`edge0-120b`](docs/models/edge0-145b.md) | coming soon | Q3_K or NVFP4 latent experts (1280), 48 layers, 512 experts, K=10 | edge0-strata ✅ (Linux or Windows, NVIDIA GPU) |
+| [`edge0-145b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-145B-NVFP4) | Q3_K or NVFP4 latent experts (1792), 48 layers, 512 experts, K=10 | edge0-strata ✅ (Linux or Windows, NVIDIA GPU) |
+| [`edge0-120b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-120B-NVFP4) | Q3_K or NVFP4 latent experts (1280), 48 layers, 512 experts, K=10 | edge0-strata ✅ (Linux or Windows, NVIDIA GPU) |
 
 The two released checkpoints are built on open sparse-MoE base models (Qwen3.6-35B-A3B
 and the Ling 3.0 bailing hybrid respectively) and ship with the

README_es.md

@@ -12,6 +12,8 @@
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--8B--A1B--preview-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-8B-A1B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--35B--A3B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--8B--A1B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--145B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-145B-q3k)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--120B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-120B-q3k)
 [![arXiv](https://img.shields.io/badge/arXiv-2609.18063-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2609.18063)
 [![GitHub](https://img.shields.io/badge/GitHub-Edge0--AI%2FEdge0-black?style=for-the-badge&logo=github)](https://github.com/Edge0-AI/Edge0)
 [![Discord](https://img.shields.io/badge/Discord-Join%20us-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://edge0.ai/discord)
@@ -23,7 +25,7 @@
 
 ## Novedades
 
-- **[2026-11-13]** Lanzamos **`edge0-145b`** y **`edge0-120b`**, modelos latent mixture-of-experts derivados de Qwen3.8-Flash-Next para una sola GPU NVIDIA en Linux o Windows. Se ejecutan en [edge0-strata](edge0-strata/EDGE0.md), que `edge0-infer serve` y `edge0-infer chat` inician para ellos; consulta la [página del modelo](docs/models/edge0-145b.md).
+- **[2026-11-13]** Lanzamos **`edge0-145b`** y **`edge0-120b`**, modelos latent mixture-of-experts derivados de Qwen3.8-Flash-Next para una sola GPU NVIDIA en Linux o Windows. Se ejecutan en [edge0-strata](edge0-strata/EDGE0.md), que `edge0-infer serve` y `edge0-infer chat` inician para ellos; consulta la [página del modelo](docs/models/edge0-145b.md). Pesos en Hugging Face: [`Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [`Edge0-145B-NVFP4`](https://huggingface.co/Edge0/Edge0-145B-NVFP4) · [`Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [`Edge0-120B-NVFP4`](https://huggingface.co/Edge0/Edge0-120B-NVFP4).
 - **[2026-11-04]** Lanzamos el **motor de inferencia unificado [`edge0-infer`](edge0-infer/README_es.md)**: construido sobre [GGML](https://github.com/ggml-org/ggml), un solo motor y una sola API nativa en C++ ejecutan el mismo modelo en macOS, iOS, Android y Windows, con una CLI, un servidor local compatible con OpenAI y ejemplos de aplicaciones de chat para escritorio, Android e iOS. La primera versión admite `edge0-35b`; `edge0-8b` llegará en una versión posterior. Sustituye al framework de Python y a los motores de cada plataforma; el código anterior se conserva en la etiqueta [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer).
 - **[2026-09-30]** Lanzamos los **motores de inferencia de edge0 para cuatro plataformas — iOS, macOS, Android y Windows** (código fuente en la etiqueta [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer)).
 - **[2026-09-16]** Nuestro informe técnico está en arXiv: [The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction](https://arxiv.org/abs/2609.18063).
@@ -78,8 +80,8 @@ las cabezas prerouter entrenadas funcionan en conjunto como una sola unidad.
 |---|---|---|---|
 | `edge0-35b` | [`Edge0/Edge0-35B-A3B-preview`](https://huggingface.co/Edge0/Edge0-35B-A3B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview) | 4 bits, 40 capas, 256 expertos, prerouter K=4 | Motor nativo ✅ |
 | `edge0-8b` | [`Edge0/Edge0-8B-A1B-preview`](https://huggingface.co/Edge0/Edge0-8B-A1B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview) | 4 bits, 24 capas, 128 expertos, prerouter K=8 | Motor nativo (previsto) |
-| [`edge0-145b`](docs/models/edge0-145b.md) | coming soon | expertos latentes Q3_K o NVFP4 (1792), 48 capas, 512 expertos, K=10 | edge0-strata ✅ (Linux o Windows, GPU NVIDIA) |
-| [`edge0-120b`](docs/models/edge0-145b.md) | coming soon | expertos latentes Q3_K o NVFP4 (1280), 48 capas, 512 expertos, K=10 | edge0-strata ✅ (Linux o Windows, GPU NVIDIA) |
+| [`edge0-145b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-145B-NVFP4) | expertos latentes Q3_K o NVFP4 (1792), 48 capas, 512 expertos, K=10 | edge0-strata ✅ (Linux o Windows, GPU NVIDIA) |
+| [`edge0-120b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-120B-NVFP4) | expertos latentes Q3_K o NVFP4 (1280), 48 capas, 512 expertos, K=10 | edge0-strata ✅ (Linux o Windows, GPU NVIDIA) |
 
 Los dos checkpoints publicados se construyen sobre modelos base sparse-MoE abiertos
 (Qwen3.6-35B-A3B y el híbrido bailing Ling 3.0, respectivamente) e incluyen
@@ -136,7 +138,7 @@ Máximo 100:
 | Model | deepswe 1.1 | terminal-bench 2.1 | AA-LCR | IFBench | GPQA-Diamond | HLE |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: |
 | **Edge0-145B** | **64.6** | **86.5** | 79.3 | 80.6 | 90.4 | 35.4 |
-| **Edge0-120B** | 50.0 | 75.0 | 81.0 | 79.3 | 87.4 | 31.0 |
+| **Edge0-120B** | 51.3 | 75.3 | 81.0 | 79.3 | 87.4 | 31.0 |
 | DeepSeek-V4-Flash-0731 | 54.4 | 82.7 | 79.7 | 79.2 | 90.8 | 33.8 |
 | Qwen3.8-Flash-Next | 58.7 | 83.3 | 79.1 | **81.3** | **91.7** | 35.9 |
 | Qwen3.8-27B | 42.2 | 73.0 | **82.0** | 79.5 | 89.2 | 30.8 |

README_fr.md

@@ -12,6 +12,8 @@
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--8B--A1B--preview-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-8B-A1B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--35B--A3B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview)
 [![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--8B--A1B--preview-624AFF?style=for-the-badge)](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--145B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-145B-q3k)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--120B-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-120B-q3k)
 [![arXiv](https://img.shields.io/badge/arXiv-2609.18063-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2609.18063)
 [![GitHub](https://img.shields.io/badge/GitHub-Edge0--AI%2FEdge0-black?style=for-the-badge&logo=github)](https://github.com/Edge0-AI/Edge0)
 [![Discord](https://img.shields.io/badge/Discord-Join%20us-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://edge0.ai/discord)
@@ -23,7 +25,7 @@
 
 ## Actualités
 
-- **[2026-11-13]** Nous avons publié **`edge0-145b`** et **`edge0-120b`**, des modèles latent mixture-of-experts dérivés de Qwen3.8-Flash-Next pour un seul GPU NVIDIA sous Linux ou Windows. Ils s'exécutent sur [edge0-strata](edge0-strata/EDGE0.md), que `edge0-infer serve` et `edge0-infer chat` démarrent pour eux ; voir la [page du modèle](docs/models/edge0-145b.md).
+- **[2026-11-13]** Nous avons publié **`edge0-145b`** et **`edge0-120b`**, des modèles latent mixture-of-experts dérivés de Qwen3.8-Flash-Next pour un seul GPU NVIDIA sous Linux ou Windows. Ils s'exécutent sur [edge0-strata](edge0-strata/EDGE0.md), que `edge0-infer serve` et `edge0-infer chat` démarrent pour eux ; voir la [page du modèle](docs/models/edge0-145b.md). Poids sur Hugging Face : [`Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [`Edge0-145B-NVFP4`](https://huggingface.co/Edge0/Edge0-145B-NVFP4) · [`Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [`Edge0-120B-NVFP4`](https://huggingface.co/Edge0/Edge0-120B-NVFP4).
 - **[2026-11-04]** Nous avons publié le **moteur d'inférence unifié [`edge0-infer`](edge0-infer/README_fr.md)** : construit sur [GGML](https://github.com/ggml-org/ggml), un seul moteur et une seule API C++ native exécutent le même modèle sur macOS, iOS, Android et Windows, avec une CLI, un serveur local compatible OpenAI et des exemples d'applications de discussion pour le bureau, Android et iOS. La première version prend en charge `edge0-35b` ; `edge0-8b` suivra dans une version ultérieure. Il remplace le framework Python et les moteurs propres à chaque plateforme ; l'ancien code est conservé au tag [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer).
 - **[2026-09-30]** Nous avons publié les **moteurs d'inférence edge0 pour quatre plateformes — iOS, macOS, Android et Windows** (code source au tag [`pre-edge0-infer`](https://github.com/Edge0-AI/Edge0/tree/pre-edge0-infer)).
 - **[2026-09-16]** Notre rapport technique est sur arXiv : [The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction](https://arxiv.org/abs/2609.18063).
@@ -79,8 +81,8 @@ une seule unité.
 |---|---|---|---|
 | `edge0-35b` | [`Edge0/Edge0-35B-A3B-preview`](https://huggingface.co/Edge0/Edge0-35B-A3B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview) | 4-bit, 40 couches, 256 experts, prerouter K=4 | Moteur natif ✅ |
 | `edge0-8b` | [`Edge0/Edge0-8B-A1B-preview`](https://huggingface.co/Edge0/Edge0-8B-A1B-preview) · [ModelScope](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview) | 4-bit, 24 couches, 128 experts, prerouter K=8 | Moteur natif (prévu) |
-| [`edge0-145b`](docs/models/edge0-145b.md) | coming soon | experts latents Q3_K ou NVFP4 (1792), 48 couches, 512 experts, K=10 | edge0-strata ✅ (Linux ou Windows, GPU NVIDIA) |
-| [`edge0-120b`](docs/models/edge0-145b.md) | coming soon | experts latents Q3_K ou NVFP4 (1280), 48 couches, 512 experts, K=10 | edge0-strata ✅ (Linux ou Windows, GPU NVIDIA) |
+| [`edge0-145b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-145B-q3k`](https://huggingface.co/Edge0/Edge0-145B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-145B-NVFP4) | experts latents Q3_K ou NVFP4 (1792), 48 couches, 512 experts, K=10 | edge0-strata ✅ (Linux ou Windows, GPU NVIDIA) |
+| [`edge0-120b`](docs/models/edge0-145b.md) | [`Edge0/Edge0-120B-q3k`](https://huggingface.co/Edge0/Edge0-120B-q3k) · [NVFP4](https://huggingface.co/Edge0/Edge0-120B-NVFP4) | experts latents Q3_K ou NVFP4 (1280), 48 couches, 512 experts, K=10 | edge0-strata ✅ (Linux ou Windows, GPU NVIDIA) |
 
 Les deux checkpoints publiés reposent sur des modèles de base sparse-MoE ouverts
 (respectivement Qwen3.6-35B-A3B et l'hybride bailing Ling 3.0) et intègrent
@@ -137,7 +139,7 @@ au-dessus de la base). Maximum 100 :
 | Model | deepswe 1.1 | terminal-bench 2.1 | AA-LCR | IFBench | GPQA-Diamond | HLE |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: |
 | **Edge0-145B** | **64.6** | **86.5** | 79.3 | 80.6 | 90.4 | 35.4 |
-| **Edge0-120B** | 50.0 | 75.0 | 81.0 | 79.3 | 87.4 | 31.0 |
+| **Edge0-120B** | 51.3 | 75.3 | 81.0 | 79.3 | 87.4 | 31.0 |
 | DeepSeek-V4-Flash-0731 | 54.4 | 82.7 | 79.7 | 79.2 | 90.8 | 33.8 |
 | Qwen3.8-Flash-Next | 58.7 | 83.3 | 79.1 | **81.3** | **91.7** | 35.9 |
 | Qwen3.8-27B | 42.2 | 73.0 | **82.0** | 79.5 | 89.2 | 30.8 |