From 0fb04f7ac35ccbc4596a0a6c2051b7d203b7f383 Mon Sep 17 00:00:00 2001 From: "LocalAI [bot]" <139863280+localai-bot@users.noreply.github.com> Date: Thu, 23 Apr 2026 23:26:27 +0200 Subject: [PATCH] chore(model-gallery): :arrow_up: update checksum (#9522) :arrow_up: Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> --- gallery/index.yaml | 106 +++++---------------------------------------- 1 file changed, 11 insertions(+), 95 deletions(-) diff --git a/gallery/index.yaml b/gallery/index.yaml index 78c3f4c9a..5c0b974b2 100644 --- a/gallery/index.yaml +++ b/gallery/index.yaml @@ -3,40 +3,7 @@ url: "github:mudler/LocalAI/gallery/virtual.yaml@master" urls: - https://huggingface.co/KyleHessling1/Qwopus-GLM-18B-Merged-GGUF - description: | - # 🪐 Qwen3.5-9B-GLM5.1-Distill-v1 - - ## 📌 Model Overview - - **Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1` - **Base Model:** Qwen3.5-9B - **Training Type:** Supervised Fine-Tuning (SFT, Distillation) - **Parameter Scale:** 9B - **Training Framework:** Unsloth - - This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**. - - The primary goals are to: - - - Improve **structured reasoning ability** - - Enhance **instruction-following consistency** - - Activate **latent knowledge via better reasoning structure** - - ## 📊 Training Data - - ### Main Dataset - - - `Jackrong/GLM-5.1-Reasoning-1M-Cleaned` - - Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset. - - Generated from a **GLM-5.1 teacher model** - - Approximately **700x** the scale of `Qwen3.5-reasoning-700x` - - Training used a **filtered subset**, not the full source dataset. - - ### Auxiliary Dataset - - - `Jackrong/Qwen3.5-reasoning-700x` - - ... + description: "# \U0001FA90 Qwen3.5-9B-GLM5.1-Distill-v1\n\n## \U0001F4CC Model Overview\n\n**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`\n**Base Model:** Qwen3.5-9B\n**Training Type:** Supervised Fine-Tuning (SFT, Distillation)\n**Parameter Scale:** 9B\n**Training Framework:** Unsloth\n\nThis model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.\n\nThe primary goals are to:\n\n - Improve **structured reasoning ability**\n - Enhance **instruction-following consistency**\n - Activate **latent knowledge via better reasoning structure**\n\n## \U0001F4CA Training Data\n\n### Main Dataset\n\n - `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`\n - Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.\n - Generated from a **GLM-5.1 teacher model**\n - Approximately **700x** the scale of `Qwen3.5-reasoning-700x`\n - Training used a **filtered subset**, not the full source dataset.\n\n### Auxiliary Dataset\n\n - `Jackrong/Qwen3.5-reasoning-700x`\n\n...\n" license: "apache-2.0" tags: - llm @@ -127,26 +94,7 @@ url: "github:mudler/LocalAI/gallery/virtual.yaml@master" urls: - https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF - description: | - # 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled - - A reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving. - - The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples. - - - **Developed by:** @hesamation - - **Base model:** `Qwen/Qwen3.6-35B-A3B` - - **License:** apache-2.0 - - This fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction. - - [](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t) - - ## Benchmark Results - - The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark. - - ... + description: "# \U0001F525 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled\n\nA reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving.\n\nThe training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples.\n\n - **Developed by:** @hesamation\n - **Base model:** `Qwen/Qwen3.6-35B-A3B`\n - **License:** apache-2.0\n\nThis fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction.\n\n[](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t)\n\n## Benchmark Results\n\nThe MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.\n\n...\n" license: "apache-2.0" tags: - llm @@ -182,40 +130,7 @@ url: "github:mudler/LocalAI/gallery/virtual.yaml@master" urls: - https://huggingface.co/Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1-GGUF - description: | - # 🪐 Qwen3.5-9B-GLM5.1-Distill-v1 - - ## 📌 Model Overview - - **Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1` - **Base Model:** Qwen3.5-9B - **Training Type:** Supervised Fine-Tuning (SFT, Distillation) - **Parameter Scale:** 9B - **Training Framework:** Unsloth - - This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**. - - The primary goals are to: - - - Improve **structured reasoning ability** - - Enhance **instruction-following consistency** - - Activate **latent knowledge via better reasoning structure** - - ## 📊 Training Data - - ### Main Dataset - - - `Jackrong/GLM-5.1-Reasoning-1M-Cleaned` - - Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset. - - Generated from a **GLM-5.1 teacher model** - - Approximately **700x** the scale of `Qwen3.5-reasoning-700x` - - Training used a **filtered subset**, not the full source dataset. - - ### Auxiliary Dataset - - - `Jackrong/Qwen3.5-reasoning-700x` - - ... + description: "# \U0001FA90 Qwen3.5-9B-GLM5.1-Distill-v1\n\n## \U0001F4CC Model Overview\n\n**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`\n**Base Model:** Qwen3.5-9B\n**Training Type:** Supervised Fine-Tuning (SFT, Distillation)\n**Parameter Scale:** 9B\n**Training Framework:** Unsloth\n\nThis model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.\n\nThe primary goals are to:\n\n - Improve **structured reasoning ability**\n - Enhance **instruction-following consistency**\n - Activate **latent knowledge via better reasoning structure**\n\n## \U0001F4CA Training Data\n\n### Main Dataset\n\n - `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`\n - Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.\n - Generated from a **GLM-5.1 teacher model**\n - Approximately **700x** the scale of `Qwen3.5-reasoning-700x`\n - Training used a **filtered subset**, not the full source dataset.\n\n### Auxiliary Dataset\n\n - `Jackrong/Qwen3.5-reasoning-700x`\n\n...\n" license: "apache-2.0" tags: - llm @@ -3845,7 +3760,7 @@ cached in the models directory like any other managed model). NON-COMMERCIAL RESEARCH USE ONLY. For commercial use see `insightface-opencv`. tags: [face-recognition, face-verification, face-embedding, research-only, gpu, cpu] - urls: [https://github.com/deepinsight/insightface] + urls: ['https://github.com/deepinsight/insightface'] overrides: backend: insightface parameters: {model: insightface-buffalo-l} @@ -3876,7 +3791,7 @@ cheaper detector — good balance on mid-range hardware. NON-COMMERCIAL RESEARCH USE ONLY. tags: [face-recognition, face-verification, face-embedding, research-only, gpu, cpu] - urls: [https://github.com/deepinsight/insightface] + urls: ['https://github.com/deepinsight/insightface'] overrides: backend: insightface parameters: {model: insightface-buffalo-m} @@ -3906,7 +3821,7 @@ genderage, ~159MB). Good fit for mid-range CPU deployments. NON-COMMERCIAL RESEARCH USE ONLY. tags: [face-recognition, face-verification, face-embedding, research-only, edge, cpu] - urls: [https://github.com/deepinsight/insightface] + urls: ['https://github.com/deepinsight/insightface'] overrides: backend: insightface parameters: {model: insightface-buffalo-s} @@ -3938,7 +3853,7 @@ only verification and embedding are needed. NON-COMMERCIAL RESEARCH USE ONLY. tags: [face-recognition, face-verification, face-embedding, research-only, edge, cpu] - urls: [https://github.com/deepinsight/insightface] + urls: ['https://github.com/deepinsight/insightface'] overrides: backend: insightface parameters: {model: insightface-buffalo-sc} @@ -3969,7 +3884,7 @@ harder benchmarks; pays for it in GPU memory. NON-COMMERCIAL RESEARCH USE ONLY. tags: [face-recognition, face-verification, face-embedding, research-only, gpu] - urls: [https://github.com/deepinsight/insightface] + urls: ['https://github.com/deepinsight/insightface'] overrides: backend: insightface parameters: {model: insightface-antelopev2} @@ -4001,7 +3916,7 @@ Weights are downloaded on install via LocalAI's gallery mechanism (~40MB). tags: [face-recognition, face-verification, face-embedding, commercial-ok, gpu, cpu] - urls: [https://github.com/opencv/opencv_zoo] + urls: ['https://github.com/opencv/opencv_zoo'] overrides: backend: insightface parameters: {model: face_detection_yunet_2023mar.onnx} @@ -4035,7 +3950,7 @@ at comparable accuracy for face tasks. APACHE 2.0 — commercial-safe. Weights are downloaded on install via LocalAI's gallery mechanism. tags: [face-recognition, face-verification, face-embedding, commercial-ok, edge, cpu] - urls: [https://github.com/opencv/opencv_zoo] + urls: ['https://github.com/opencv/opencv_zoo'] overrides: backend: insightface parameters: {model: face_detection_yunet_2023mar_int8.onnx} @@ -15923,6 +15838,7 @@ uri: "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors" - filename: "umt5-xxl-encoder-Q8_0.gguf" uri: "huggingface://city96/umt5-xxl-encoder-gguf/umt5-xxl-encoder-Q8_0.gguf" + sha256: 2521d4de0bf9e1cc6549866463ceae85e4ec3239bc6063f7488810be39033bbc - filename: "clip_vision_h.safetensors" uri: "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/clip_vision/clip_vision_h.safetensors" - name: sd-1.5-ggml