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  • (Voiceover) OLMo 2 and building effective teams for training language models

    (Voiceover) OLMo 2 and building effective teams for training language models
    2024/11/26

    Full post:

    https://www.interconnects.ai/p/olmo-2-and-building-language-model-training

    OLMo 2 demo: https://playground.allenai.org/

    OLMo 2 artifacts: https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc

    Chapters

    00:00 Building AI Teams

    06:35 OLMo 2

    Figures

    Fig 1, pretrain plot: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/pretrain.webp

    Fig 2, pretrain table: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/pretrain-table.webp

    Fig 3, post-train table: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/olmo2/postrain-table.webp



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    10 分
  • (Voiceover) Tülu 3: The next era in open post-training

    (Voiceover) Tülu 3: The next era in open post-training
    2024/11/21

    Original post: https://www.interconnects.ai/p/tulu-3

    Chapters

    00:00 History

    05:44 Technical details sneak peak

    Figures

    Fig 1, results: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/tulu3-img/results.webp

    Fig 2, overview: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/tulu3-img/overview.webp

    Fig 3, preferences: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/tulu3-img/preferences.webp

    Fig 4, RLVR: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/tulu3-img/rlvr.webp



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    8 分
  • (Voiceover) Scaling realities

    (Voiceover) Scaling realities
    2024/11/14

    Original post: https://www.interconnects.ai/p/scaling-realities



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    4 分
  • (Voiceover) Saving the National AI Research Resource & my AI policy outlook

    (Voiceover) Saving the National AI Research Resource & my AI policy outlook
    2024/11/13

    Original post: https://www.interconnects.ai/p/saving-the-nairr

    Chapters

    05:26: Do we need an AI research resource or an LM research resource?

    08:59: Policy roundups



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    11 分
  • Interviewing Tim Dettmers on open-source AI: Agents, scaling, quantization and what's next

    Interviewing Tim Dettmers on open-source AI: Agents, scaling, quantization and what's next
    2024/11/07
    Tim Dettmers does not need an introduction for most people building open-source AI. If you are part of that minority, you’re in for a treat. Tim is the lead developer behind most of the open-source tools for quantization: QLoRA, bitsandbytes, 4 and 8 bit inference, and plenty more. He recently finished his Ph.D. at the University of Washington, is now a researcher at the Allen Institute for AI, and is starting as a professor at Carnegie Mellon University in fall of 2025.Tim is a joy to talk to. He thinks independently on all the AI issues of today, bringing new perspectives that challenge the status quo. At the same time, he’s sincere and very helpful to work with, working hard to uplift those around him and the academic community. There’s a reason he’s so loved in the open-source AI community.Find more about Tim on his Twitter or Google Scholar. He also has a great blog where he talks about things like which GPUs to buy and which grad school to choose.Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.Show NotesHere's a markdown list of companies, people, projects, research papers, and other key named entities mentioned in the transcript:* QLoRA* Bits and Bytes* Llama 3* Apple Intelligence* SWE Bench* RewardBench* Claude (AI assistant by Anthropic)* Transformers (Hugging Face library)* Gemma (Google's open weight language model)* Notebook LM* LangChain* LangGraph* Weights & Biases* Blackwell (NVIDIA GPU architecture)* Perplexity* Branch Train Merge (research paper)* "ResNets do iterative refinement on features" (research paper)* CIFAR-10 and CIFAR-100 (computer vision datasets)* Lottery Ticket Hypothesis (research paper)* OpenAI O1* TRL (Transformer Reinforcement Learning) by Hugging Face* Tim's work on quantization (this is just one example)Timestamps* [00:00:00] Introduction and background on Tim Dettmers* [00:01:53] Future of open source AI models* [00:09:44] SWE Bench and evaluating AI systems* [00:13:33] Using AI for coding, writing, and thinking* [00:16:09] Academic research with limited compute* [00:32:13] Economic impact of AI* [00:36:49] User experience with different AI models* [00:39:42] O1 models and reasoning in AI* [00:46:27] Instruction tuning vs. RLHF and synthetic data* [00:51:16] Model merging and optimization landscapes* [00:55:08] Knowledge distillation and optimization dynamics* [01:01:55] State-space models and transformer dominance* [01:06:00] Definition and future of AI agents* [01:09:20] The limit of quantizationTranscript and full details: https://www.interconnects.ai/p/tim-dettmersGet Interconnects (https://www.interconnects.ai/)...... on YouTube: https://www.youtube.com/@interconnects... on Twitter: https://x.com/interconnectsai... on Linkedin: https://www.linkedin.com/company/interconnects-ai... on Spotify: https://open.spotify.com/show/2UE6s7wZC4kiXYOnWRuxGv… on Apple Podcasts: https://podcasts.apple.com/us/podcast/interconnects/id1719552353 Get full access to Interconnects at www.interconnects.ai/subscribe
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    1 時間 16 分
  • Interviewing Andrew Carr of Cartwheel on the State of Generative AI

    Interviewing Andrew Carr of Cartwheel on the State of Generative AI
    2024/10/31
    Andrew Carr is co-founder and chief scientist at Cartwheel, where he is building text-to-motion AI models and products for gaming, film, and other creative endeavors. We discuss how to keep generative AI fun and expansive — niche powerful use-cases, AI poetry, AI devices like Meta RayBans, generalization to new domains like robotics, and building successful AI research cultures.Andrew is one of my well read friends on the directions AI is going, so it is great to bring him in for an official conversation. He spent time at OpenAI working on Codex, Gretel AI, and is an editor for the TLDR AI Newsletter.Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.Show NotesNamed entities and papers mentioned in the podcast transcript:* Codex and GitHub Copilot* Gretel AI* TLDR AI Newsletter* Claude Computer Use* Blender 3D simulator* Common Sense Machines* HuggingFace Simulate, Unity, Godot* Runway ML* Mark Chen, OpenAI Frontiers Team Lead* Meta’s Lingua, Spirit LM, torchtitan and torchchat* Self-Rewarding Language Models paper* Meta Movie Gen paperTimestamps* [00:00] Introduction to Andrew and Cartwheel* [07:00] Differences between Cartwheel and robotic foundation models* [13:33] Claude computer use* [18:45] Supervision and creativity in AI-generated content* [23:26] Adept AI and challenges in building AI agents* [30:56] Successful AI research culture at OpenAI and elsewhere* [38:00] Keeping up with AI research* [44:36] Meta Ray-Ban smart glasses and AI assistants* [51:17] Meta's strategy with Llama and open source AITranscript & Full Show Notes: https://www.interconnects.ai/p/interviewing-andrew-carr Get full access to Interconnects at www.interconnects.ai/subscribe
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    54 分
  • (Voiceover) Why I build open language models

    (Voiceover) Why I build open language models
    2024/10/30

    Full post:

    https://www.interconnects.ai/p/why-i-build-open-language-models



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    10 分
  • (Voiceover) Claude's agentic future and the current state of the frontier models

    (Voiceover) Claude's agentic future and the current state of the frontier models
    2024/10/23

    How Claude's computer use works. Where OpenAI, Anthropic, and Google all have a lead on eachother.

    Original post: https://www.interconnects.ai/p/claudes-agency

    Chapters

    00:00 Claude's agentic future and the current state of the frontier models

    04:43 The state of the frontier models

    04:49 1. Anthropic has the best model we are accustomed to using

    05:27 Google has the best small & cheap model for building automation and basic AI engineering

    08:07 OpenAI has the best model for reasoning, but we don’t know how to use it

    09:12 All of the laboratories have much larger models they’re figuring out how to release (and use)

    10:42 Who wins?

    Figures

    Fig 1, Sonnet New Benchmarks: https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d2e63ff-ac9f-4f8e-9749-9ef2b9b25b6c_1290x1290.png

    Fig 2, Sonnet Old Benchmarks: https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4bccbd4d-f1c8-4a38-a474-69a3df8a4448_2048x1763.png

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    … on Apple Podcasts: https://podcasts.apple.com/us/podcast/interconnects/id1719552353



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    11 分