We're so unfathomably back! https://
huggingface.co/Qwen/QVQ-72B-P
review
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@reach_vb
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Qwen Releases QVQ-72B-Preview: Advanced Multimodal AI Model
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QVQ-72B Preview Model Now Available on Hugging Face
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Directly play with the model here: https://
huggingface.co/spaces/Qwen/QV
Q-72B-preview
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Qwen QvQ 72B: Advanced Reasoning Model With Vision Capabilities
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Qwen released QvQ 72B OpenAI o1 like reasoning model on Hugging Face with Vision capabilities – beating GPT4o, Claude Sonnet 3.5
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Making AI Models More Accessible: Code Snippets and Implementation
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Thanks for feedback, have you tried the “use this model” button on the model pages? It usually has snippets for how to run the model via various code snippets etc Keen to hear what would make this more useful for you/ community.
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Supporting Open Source Projects with Compute and Monetary Grants
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We’ll continue supporting open source projects both via compute and monetary grants! Always open to suggestions on which ones we should support more!
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Ownership Rights of AI Models and Training Data
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Repeat after me: NOT YOUR WEIGHTS, NOT YOUR BRAIN! https://t.co/NAKaZTavci
— Vaibhav (VB) Srivastav (@reach_vb) 23 décembre 2024Repeat after me: NOT YOUR WEIGHTS, NOT YOUR BRAIN!
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Ollama Now Supports Private GGUF Models from Hugging Face
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Not your weights, not your brain! Starting today, you can run your private GGUFs from the Hugging Face hub directly in @ollama! 🔥
— Vaibhav (VB) Srivastav (@reach_vb) 23 décembre 2024
You asked, we delivered!
Works out of the box, all you need to do is add your Ollama SSH key to your profile, and that's it! ⚡
Run private… pic.twitter.com/v832KNilLCNot your weights, not your brain! Starting today, you can run your private GGUFs from the Hugging Face hub directly in @ollama
! You asked, we delivered! Works out of the box, all you need to do is add your Ollama SSH key to your profile, and that's it! Run private -
Open Weight Model Deployment Alternative to Closed Source APIs
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Open Science FTW! That's a fully open weight model, that you can deploy wherever you want, however you want mogging closed source APIs!
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Training AI Models with Preference Pair Selection Methods
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Dataset: Create preference pairs by selecting a chosen response from the positive set and a negative response from the negative set, which is used to train the model on relative preferences