Right now, single-turn tasks only, but I will very likely expand this to multi-turn generation soon!
LLMS
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Building AI Systems with Open-Source Models Only
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Yep! You can even do it entirely w/o any closed-source models in the loop! In the demo above, it was powered entirely by Kimi, Deepseek, and Qwen.
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Codestral 25.08 and Complete Mistral Coding Stack Launch
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Meet the new Codestral 25.08 and the Complete Mistral Coding Stack for Enterprises!
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Training AI Models for Under One Dollar Now Possible
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Yep! And you can train a model for literally $1 or less
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OpenRouter Credits Enable Cost-Effective AI Model Execution
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For this run, I spent $0 on the GPU, and 40 cents on OpenRouter credits for prompt generation, RULER ranking, etc.
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Early LSTM Language Model Architecture Recognition from NLP Community
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Special thanks to the NLP community for the recognition. This was my earliest work in "large" language model at the time , quoted from the paper "Our stacking LSTM models have 4 layers, each with 1000 cells, and 1000-dimensional embeddings" .
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OpenPipe’s RULER Enables LLM Training Without Labeled Data
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Under the hood, this is powered by @OpenPipeAI
's ART library. Their RULER technique made this possible, as it doesn't require labeled output data. With no need for labeled outputs, we can just sample a LLM to generate scenarios to RL against. Absolutely crazy! -
Fast AI Model Training System Delivers Impressive Grammar Results
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It's incredible how quickly you can go from an idea to a fully-trained model with this system. And the resulting models are really good! The grammar model in the demo video works really well, yet it was trained for just minutes, and was only 1.5B params 🙂
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AutoRL: Train AI Models for Any Task via GitHub
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If you'd like to try it or contribute, check out the Github repo and notebook: https://
github.com/OpenPipe/ART/t
ree/auto-rl?tab=readme-ov-file#-autorl-train-models-for-any-task
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