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reacted to merve's post with πŸ‘ about 19 hours ago
This week in open AI was πŸ”₯ Let's recap! πŸ€— https://huggingface.co/collections/merve/january-31-releases-679a10669bd4030090c5de4d LLMs πŸ’¬ > Huge: AllenAI released new TΓΌlu models that outperform DeepSeek R1 using Reinforcement Learning with Verifiable Reward (RLVR) based on Llama 3.1 405B πŸ”₯ > Mistral AI is back to open-source with their "small" 24B models (base & SFT), with Apache 2.0 license 😱 > Alibaba Qwen released their 1M context length models Qwen2.5-Instruct-1M, great for agentic use with Apache 2.0 license πŸ”₯ > Arcee AI released Virtuoso-medium, 32.8B LLMs distilled from DeepSeek V3 with dataset of 5B+ tokens > Velvet-14B is a new family of 14B Italian LLMs trained on 10T tokens in six languages > OpenThinker-7B is fine-tuned version of Qwen2.5-7B-Instruct on OpenThoughts dataset VLMs & vision πŸ‘€ > Alibaba Qwen is back with Qwen2.5VL, amazing new capabilities ranging from agentic computer use to zero-shot localization πŸ”₯ > NVIDIA released new series of Eagle2 models with 1B and 9B sizes > DeepSeek released Janus-Pro, new any-to-any model (image-text generation from image-text input) with MIT license > BEN2 is a new background removal model with MIT license! Audio πŸ—£οΈ > YuE is a new open-source music generation foundation model, lyrics-to-song generation Codebase πŸ‘©πŸ»β€πŸ’» > We are open-sourcing our SmolVLM training and eval codebase! https://github.com/huggingface/smollm/tree/main/vision > Open-R1 is open-source reproduction of R1 by @huggingface science team https://huggingface.co/blog/open-r1
reacted to chansung's post with πŸ‘ about 19 hours ago
A brief summary of the o3-mini The OpenAI o3-mini model is a significant improvement over the o1-mini, reaching o1 performance levels. While generally good, its performance isn't universally better than previous models (o1, o1-prev.) or GPT-4o across all benchmarks. This means workflows should be re-evaluated with each model upgrade. The o3-mini has "low," "medium," and "high" versions, with "low" being the base model used for benchmarking. It's speculated that the higher versions simply involve more processing. A fair comparison with other models like Gemini 2.0 Thinking or DeepSeek-R1 would likely need to use the "low" version and a similar "think more" mechanism. The system card is recommended reading due to its comprehensive benchmark data. https://openai.com/index/openai-o3-mini/
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