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Tyler Williams PRO

unmodeled-tyler
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https://unmodeledtyler.substack.com

AI & ML interests

AI research engineer & solo operator of VANTA Research

Recent Activity

reacted to alvdansen's post with 🚀 3 days ago
Releasing Flimmer today — a video LoRA training toolkit for WAN 2.1 and 2.2 that covers the full pipeline from raw footage to trained checkpoint. The standout feature is phased training: multi-stage runs where each phase has its own learning rate, epochs, and dataset, with the checkpoint carrying forward automatically. Built specifically with WAN 2.2's dual-expert MoE architecture in mind. Data prep tools are standalone and output standard formats — they work with any trainer, not just Flimmer. Early release, building in the open. LTX support coming next. http://github.com/alvdansen/flimmer-trainer
reacted to umarbutler's post with 🔥 3 days ago
This awesome visualization by @abdurrahmanbutler tracks how reliant the High Court of Australia has been on UK precedents over time. Back in the early 1900s, up to 70% of citations in High Court decisions were from the UK. Today, that number sits around 20%. This change seems to have happened gradually as Australia gained more and more independence from the UK, culminating in the Australia Acts of 1986, where we see a nice bump in the proportion of Australian cases cited. These insights would not be possible without our latest legal AI model, Kanon 2 Enricher, which we used to extract dates and citations from High Court decisions in https://huggingface.co/datasets/isaacus/open-australian-legal-corpus and categorize citations by jurisdiction. You can learn about Kanon 2 Enricher here: https://isaacus.com/blog/kanon-2-enricher.
replied to their post 6 days ago
Link to Repo: https://github.com/unmodeled-tyler/thought-tracer I had a great time at Mistral's Hackathon in SF over the weekend! There were a lot of incredibly talented builders there and it was an honor to be a part of it! 😄 I built Thought Tracer - a TUI-based logitlens application for Ministral 3B/8B with optional AI analysis from Mistral Large on the Mistral API. Thought Tracer allows you to see what the model "believes" at each layer until it arrives at it's final next token prediction. The Entropy tab displays entropy through each layer, additionally providing both token-level and prompt-level risk for hallucination. If you have a Mistral API key, the AI analysis section is actually pretty cool because it's returned in rendered markdown and easily understandable language - providing a commentary on how the model likely arrived at it's final prediction, and also offering diagnostics for model developers. This commentary actually makes the tool pretty beginner friendly to anyone interested in exploring AI research tools for the first time. Check it out if you're interested!
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