Beats GPT4o, rivals Claude Sonnet – can run on the cheapest GPU VPS available! Open source is the way to go!
OPEN SOURCE
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Qwen2.5-Coder-32B: first open-source model to match GPT-4o
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> Qwen2.5-Coder-32B: new best-in-class open coding model, beats GPT-4o on most coding benchmarks! It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities! Completes the previous two Qwen 2.5 Coder release with 4
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Kinetix: Open-ended Physics-based RL Universe for General Agent Training
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pic.twitter.com/mJAzmdGvbt Kinetix: an open-ended universe of physics-based tasks for RL. They use Kinetix to train a general agent on millions of randomly generated physics problems and show that this agent generalises to unseen handmade environments
— Chubby♨️ (@kimmonismus) 11 novembre 2024Kinetix: an open-ended universe of physics-based tasks for RL. They use Kinetix to train a general agent on millions of randomly generated physics problems and show that this agent generalises to unseen handmade environments
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32B Open Source LLM Fits Affordable GPU Hardware
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If this doesn’t make you bullish on Open Source – I don’t know what will! That’s a 32B LLM that can easily fit on a ~0.8 USD/ hour GPU – spitting ungodly num of tokens Back of the napkin math:
– fp16/ bf16 – 32GB VRAM (would fit on a L40S)
– 8-bit – 16GB VRAM (L4)
– 4-bit – -
Open Weights Models Represent Major Victory for AI Community
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Not really – open weights is a huge win in itself.
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Gated Repositories and Access Control for AI Model Weights
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That’s precisely why we have gated repos and then we could have a Sign in with HF to ensure only people on ACL are able to access the weights.
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Cornell CS5785 Applied Machine Learning Course with 80 Video Tutorials
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Applied Machine Learning – Cornell CS5785 "Starting from the very basics, covering all of the most important ML algorithms and how to apply them in practice. Executable Jupyter notebooks (and as slides)". 80 videos!! Videos: https://
youtube.com/playlist?list=
PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83
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Code: https://
github.com/kuleshov/corne
ll-cs5785-2020-applied-ml
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Google DeepMind Model Weights Distribution Process and Timeline
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The model weights require you to fill a Google Form and wait for 3-4 business days, but hoping @GoogleDeepMind brings the model weights over to the Hub.
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AlphaFold3 inference codebase released by Google DeepMind
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Inference codebase: https://
github.com/google-deepmin
d/alphafold3
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Hugging Face Hub hosts model weights with gated repository access
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Congratulations on the release – Would love to help get these model weights on the Hugging Face hub. We can set up gated repositories so you have full ACL of who accesses the model repositories as well. (We do the same for Gemma repos as well, lmk if that’s helpful)