𝚐𝔪𝟾𝚡𝚡𝟾

@gm8xx8

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World Labs is out of stealth, with a team of brilliant minds focused on spatial intelligence. https://www.worldlabs.ai/about

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World Labs is out of stealth, with a team of brilliant minds focused on spatial intelligence. https://www.worldlabs.ai/about

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DataGemma release DataGemma, open models, address hallucination challenges by grounding LLMs in real-world statistical data from Google’s Data Commons, using RIG and RAG techniques for fact-checking and responsible AI development. blog: https://blog.google/technology/ai/google-datagemma-ai-llm/ https://huggingface.co/collections/google/datagemma-release-66df7636084d2b150a4e6643

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The shift toward smaller, more efficient specialized models continues, moving away from the old approach of using large models for everything in single pass, and instead focusing on sequential inference for better performance.

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o1-mini scored the same as GPT-4O on aider's code editing benchmark.

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o1-mini-2024-09-12 Performance on BigCodeBench-Hard: - Complete: 27.0% - Instruct: 27.7% - Average: 27.4% o1-preview-2024-09-12 Performance on BigCodeBench-Hard: - Complete: 34.5% (slightly better than Claude-3.5-Sonnet-20240620) - Instruct: 23.0% (☹︎ ☹︎ ☹︎) - Average: 28.8% both with underwhelming results on the SWE-bench and a clear performance gap between pre- and post-mitigation. Notably, o1-mini outperformed o1-preview in SQL-eval, excelling at converting natural language into SQL queries. While o1-preview handles complex questions well, it tends to overthink simpler ones, leading to mistakes. In contrast, o1-mini is less effective with complex questions but consistently gets simpler ones right. additionally, with temperature=1, the results vary across different runs. 🔗: https://openai.com/index/openai-o1-system-card/

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OpenAI’s o1 update enhances reasoning through reinforcement learning, enabling step-by-step problem-solving similar to human thought. The longer it “thinks,” the better it performs, it introduces a new scaling paradigm beyond pretraining. Rather than relying solely on prompting, o1’s chain-of-thought reasoning improves with adaptive compute, which can be scaled at inference time. - o1 outperforms GPT-4o in reasoning, ranking in the 89th percentile on Codeforces. - It uses chain-of-thought to break down problems, correct errors, and adapt, though some specifics remain unclear. - Excels in areas like data analysis, coding, and math. - o1-preview and o1-mini models are available now, with evals proving it’s not just a one-off improvement. Trusted API users will have access soon. - Results on AIME and GPQA are strong, with o1 showing significant improvement on complex prompts where GPT-4o struggles. - The system card (https://openai.com/index/openai-o1-system-card/) showcases o1’s best capabilities.

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Qwen2-VL > Pixtral

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Fish Speech 1.4 is now open-source. What’s new: - Trained on 700k hours of multilingual data (up from 200k) - Supports 8 languages: English, Chinese, German, Japanese, French, Spanish, Korean, and Arabic - Fully open-source for developers and researchers Key features: - Fast TTS with low latency - Instant voice cloning - Self-host or use cloud service - Flat-rate pricing playground → https://fish.audio

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we are not the same.

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I like when Mistral casually drops magnet links ☺︎ Mistral releases Pixtral, a 12B VLM with a Mistral Nemo 12B text backbone and 400M Vision Adapter. It processes 1024x1024 images with 16x16 pixel patches, has a 131,072 word vocabulary, and includes special tokens like img, img_break, and img_end. The model uses bf16 weights, GeLU for the vision adapter, and 2D RoPE for the encoder. I’m looking forward to the inference code release!

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Mistral released Pixtral 12B, a Vision Language Model with a 12B text backbone and 400M vision adapter. It supports larger vocabularies, new image tokens, processes 1024x1024 images, and uses bf16 weights. Mistral back at it again 🔥 🔗: https://x.com/mistralai/status/1833758285167722836?s=46 🤗: https://huggingface.co/mistral-community/pixtral-12b-240910

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Writing with AI Five ways professional writers are leveraging ChatGPT 🔗: https://openai.com/chatgpt/use-cases/writing-with-ai/

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Open Interpreter 01 App ↓ code: https://github.com/OpenInterpreter/01-app app: https://apps.apple.com/us/app/01-light/id6601937732 blog: https://changes.openinterpreter.com/log/01-app

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They trained a diffusion policy using 26,000 demonstrations across 5 tasks on the Aloha 2 robot, relying solely on imitation learning. The model has 217 million parameters, and training involved 2 million steps with 64 TPUv5e. Absolute 🔥 🔗: https://openreview.net/pdf?id=gvdXE7ikHI 𝕏: https://x.com/remicadene/status/1832911834208149683?s=46

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how far could humans go with the perfect curriculum?

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