mlx-community/Olmo-3.1-32B-Instruct-bf16
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pascalmusabyimana
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computer vision, nlp , machine learning and deeplearning
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🚀 Introducing VideoCoF: Unified Video Editing with a Temporal Reasoner (Chain-of-Frames)!
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
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2970
🚀 Introducing VideoCoF: Unified Video Editing with a Temporal Reasoner (Chain-of-Frames)!
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
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Qwen3-Next can now be Run locally! (30GB RAM)
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
💜 Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
💜 Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
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sergiopaniego's
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18 days ago
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nanochat is now in transformers!
The LLM by @karpathy is officially in the library, and we wrote a blog covering: how did we port the model, differences from the original, and how to run or train it.
go read it 🤓
nanochat-students/transformers
The LLM by @karpathy is officially in the library, and we wrote a blog covering: how did we port the model, differences from the original, and how to run or train it.
go read it 🤓
nanochat-students/transformers
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Z-Image Turbo LoRA training with AI Toolkit and Z-Image ControlNet Full Tutorial for Highest Quality : https://www.youtube.com/watch?v=ezD6QO14kRc
Z-Image Turbo LoRA training with Ostris AI Toolkit + Z-Image Turbo Fun Controlnet Union + 1-click to download and install the very best Z-Image Turbo presets. In this tutorial, I will explain how to setup Z-Image Turbo model properly in your local PC with SwarmUI and download models and use them with highest quality via ready presets. Moreover, I will show to install Z-Image Turbo Fun Controlnet Union to generate amazing quality images with ControlNet preprocessors. Furthermore, I will show how to 1-click install AI Toolkit from Ostris and train Z-Image Turbo model LoRAs with highest quality configs made for every GPU like 8 GB GPUs, 12 GB GPUs, 24 GB GPUs and so on. I did a massive research to prepare these Z-Image Turbo model training configurations.
👇 Links & Resources Mentioned:
Download SwarmUI & Models: [ https://www.patreon.com/posts/Download-SwarmUI-Models-114517862 ]
Ostris AI Toolkit (SECourses Version): [ https://www.patreon.com/posts/Ostris-AI-Toolkit-140089077 ]
Ultimate Batch Image Processing App: [ https://www.patreon.com/posts/Ultimate-Batch-Image-Processing-App-120352012 ]
SwarmUI with ComfyUI Backend Windows Tutorial: [ https://youtu.be/c3gEoAyL2IE ]
SwarmUI with ComfyUI Backend RunPod and Massed Compute Cloud Tutorial: [ https://youtu.be/bBxgtVD3ek4 ]
⏱️ Video Chapters:
00:00:00 Introduction to Z-Image Turbo Model
00:00:54 FP8 Scaled Version 5.7GB for Low VRAM
00:01:10 ControlNet Union with Z-Image Turbo
00:01:30 LoRA Training with Ostris AI Toolkit
00:02:00 Default vs Custom Training Preset Quality Comparison
00:03:00 RunPod Cloud Training Preview
00:03:40 MassedCompute Cloud Training Preview
00:04:16 Downloading Z-Image Models via SwarmUI
00:05:00 Z-Image Turbo Core Bundle & ControlNet Files
00:05:58 FP8 Scaled Model & Musubi Tuner Converter
...
Z-Image Turbo LoRA training with Ostris AI Toolkit + Z-Image Turbo Fun Controlnet Union + 1-click to download and install the very best Z-Image Turbo presets. In this tutorial, I will explain how to setup Z-Image Turbo model properly in your local PC with SwarmUI and download models and use them with highest quality via ready presets. Moreover, I will show to install Z-Image Turbo Fun Controlnet Union to generate amazing quality images with ControlNet preprocessors. Furthermore, I will show how to 1-click install AI Toolkit from Ostris and train Z-Image Turbo model LoRAs with highest quality configs made for every GPU like 8 GB GPUs, 12 GB GPUs, 24 GB GPUs and so on. I did a massive research to prepare these Z-Image Turbo model training configurations.
👇 Links & Resources Mentioned:
Download SwarmUI & Models: [ https://www.patreon.com/posts/Download-SwarmUI-Models-114517862 ]
Ostris AI Toolkit (SECourses Version): [ https://www.patreon.com/posts/Ostris-AI-Toolkit-140089077 ]
Ultimate Batch Image Processing App: [ https://www.patreon.com/posts/Ultimate-Batch-Image-Processing-App-120352012 ]
SwarmUI with ComfyUI Backend Windows Tutorial: [ https://youtu.be/c3gEoAyL2IE ]
SwarmUI with ComfyUI Backend RunPod and Massed Compute Cloud Tutorial: [ https://youtu.be/bBxgtVD3ek4 ]
⏱️ Video Chapters:
00:00:00 Introduction to Z-Image Turbo Model
00:00:54 FP8 Scaled Version 5.7GB for Low VRAM
00:01:10 ControlNet Union with Z-Image Turbo
00:01:30 LoRA Training with Ostris AI Toolkit
00:02:00 Default vs Custom Training Preset Quality Comparison
00:03:00 RunPod Cloud Training Preview
00:03:40 MassedCompute Cloud Training Preview
00:04:16 Downloading Z-Image Models via SwarmUI
00:05:00 Z-Image Turbo Core Bundle & ControlNet Files
00:05:58 FP8 Scaled Model & Musubi Tuner Converter
...
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The new Mistral 3 models are here !
Today, we announce Mistral 3, the next generation of Mistral models. Mistral 3 includes three state-of-the-art small, dense models (14B, 8B, and 3B) and Mistral Large 3 – our most capable model to date – a sparse mixture-of-experts trained with 41B active and 675B total parameters.
All models are released under the Apache 2.0 license.
Ministrals :
https://huggingface.co/collections/mistralai/ministral-3
Mistral Large 3:
https://huggingface.co/collections/mistralai/mistral-large-3
Today, we announce Mistral 3, the next generation of Mistral models. Mistral 3 includes three state-of-the-art small, dense models (14B, 8B, and 3B) and Mistral Large 3 – our most capable model to date – a sparse mixture-of-experts trained with 41B active and 675B total parameters.
All models are released under the Apache 2.0 license.
Ministrals :
https://huggingface.co/collections/mistralai/ministral-3
Mistral Large 3:
https://huggingface.co/collections/mistralai/mistral-large-3
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