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Transformer Lab – Download, interact, and train models locally

Hacker News

Transformer Lab – Download, interact, and train models locally

Transformer Lab is an open source tool that lets you interact, train, and evaluate LLMs on your own machine or in the cloud (currently Mac or Linux, Windows soon) through a simple GUI. Try it out here: https://github.com/transformerlab/transformerlab-app One of our goals is to make it as easy as possible to get started experimenting with LLMs regardless of what platform you have (even if you don’t have a GPU). You can do everything through the UI but it is built on an API that can be extended using plugins. When you start the app it will try to figure out what kind of system you have and install the right set of initial plugins to get you going optimally. Some things you can do locally: - Download and interact with LLMs - Fine tune and evaluate your own models - Export models to formats that will allow you to run in smaller memory or without a GPU (e.g. export to GGUF or MLX) It comes with a library of models and datasets to get going easily, but you can also download any model shared on Hugging Face and build your own training data. Why we built this: we have been really interested in research and experimentation with LLMs but what we found was that the tooling for going deeper is lacking — Transformer Lab was built to address that problem. We are a small, unfunded team and we’re excited to build a lot more on top of this platform. So any feedback on how to simplify getting started or how to make this more valuable for experimenting locally would be really appreciated.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, open source · Missing: https docs, just released, exist
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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