Cameron R. Wolfe, Ph.D. @cwolferesearch
Director of AI @RebuyEngine • Writer @ Deep (Learning) Focus • PhD @optimalab1 • I make AI understandable cameronrwolfe.me Joined August 2021-
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Mixture of experts, or MoE, is gaining traction as a new paradigm in model architecture. @cwolferesearch, Director of AI at Rebuy, breaks down how MoE works. stackoverflow.blog/2024/04/04/how…
Announcing the alpha release of torchtune! torchtune is a PyTorch-native library for fine-tuning LLMs. It combines hackable memory-efficient fine-tuning recipes with integrations into your favorite tools. Get started fine-tuning today! Details: hubs.la/Q02t214F0
Jim Fan @DrJimFan
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10K Followers 635 Following Founders investing in founders. We're an independent VC firm pushing the conventional boundaries of early stage investing.Persistent storage, flexible switching between CPU multi-GPU machines, VSCode, scaling training runs via multi-node jobs ... what's not to love 😊
A while ago I complained here about persistent storage in Google Colab. Have been using @LightningAI Studios for a while now for: - Full VSCode (incl. GH Copilot) - Persisted files shared across notebooks - Multi-GPU/node (!!) It's been great. Feels like a remote ML workstation
@cwolferesearch @iScienceLuvr @PyTorch IMO I love that they did from a learning perspective (which may be some of what they’re after here).
The @PyTorch team is developing a library for large model training called torchtitan 👀 They have scripts to train Llama-3 from scratch The library went public today on GitHub but it is still in pre-release state & active development Check it out → github.com/pytorch/torcht…
We are very excited that our first GH200 nodes have arrived in TACC for our GenAI center. Here is one. Fun facts: NVIDIA makes GH200 'superchips' (i.e. modules), a GH200 DGX box and a GH200 rack, which are all different. As Dan Stanzione, our TACC director, kindly explained…
A while ago I complained here about persistent storage in Google Colab. Have been using @LightningAI Studios for a while now for: - Full VSCode (incl. GH Copilot) - Persisted files shared across notebooks - Multi-GPU/node (!!) It's been great. Feels like a remote ML workstation
RESEARCH OPPORTUNITY ALERT. If you're interested in synthetic data, we're recruiting for a Research Scholar to collaborate with Cohere For AI for a 6-month internship. Must be available full-time starting ASAP. DM if you're interested 🥰.
@cwolferesearch The details are probably buried in the Delight paper, which I need to read in detail some time.
@cwolferesearch @natolambert Ideally there will be distribution shift while learning the reward model for online rlhf while using data from any policy arxiv.org/abs/2308.02585
Just read Apple's "OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework". Similar to the OLMo, it's refreshing to see an LLM paper that shares details discussing the architecture, training methods, and training data. Let's start with the…
Yesterday, we open sourced the Cohere Toolkit. We think this will be a major accelerant for getting LLMs into production within enterprise. github.com/cohere-ai/cohe…
More work coming up & we are hiring: openai.com/careers/search…
Introducing the Instruction Hierarchy, our latest safety research to advance robustness for prompt injections and other ways of tricking LLMs into executing unsafe actions. More details: arxiv.org/abs/2404.13208
AGI has become a strained symbol in the AI ecosystem, but it doesn't need to be: * The strongest idea leaders use to discuss strategy * A focal point on agency and feedback properties of AI (yes both) * A moving target * A faith movement * The shadow of RL progress pre 2020
AGI is what you want it to be Certain definitions of AGI are backing people into a pseudo-religious corner. interconnects.ai/p/agi-is-what-…
Apple presents OpenELM - An efficient LM family with open-source training and inference framework - Performs on par with OLMo while requiring 2x fewer pre-training tokens repo: github.com/apple/corenet hf: huggingface.co/apple/OpenELM abs: arxiv.org/abs/2404.14619
SnapKV: LLM Knows What You are Looking for Before Generation - Automatically compresses KV caches - Consistent decoding speed with a 3.6x increase in generation speed and an 8.2x enhancement in memory efficiency repo: github.com/FasterDecoding… abs: arxiv.org/abs/2404.14469
I just published Understanding what software developers want from AI link.medium.com/omebx3vg3Ib
Sounds like you need @datologyai
We are looking for someone to help with data engineering: managing, filtering, de-duping large scale datasets. A good, clever, resourceful hacker that knows Python is ideal. You'll get to work with our research team and learn how to train foundation models. Please DM. Remote…
Won the GPU lottery and got an 8x H100 SXM on runpod. I don't think I can ever go back Run that was going to take 8 hours on 4x RTX 6000 Ada took 15 minutes on the H100s with FSDP 15 minutes of compute to go to Llama3 => GPT-4ish performance on specific tasks. 15 minutes!
FYI I also added @cohere's command r plus. Just below llama 3 -- we're starting to get decent judges with open weights. vLLM script for running it is in RewardBench repo :)
llama 3 looking pretty good for quick llm-as-a-judge. much cheaper than openai to run it yourself with vllm. 400b will make us not need to use OpenAI for evals?