open d1 decision models are here!
• small enough to run them on my macbook
• multimodal for fun things like video input
here i'm using d1-3B to rate how cute my outfit is.
don't follow me for more fashion advice 💜
see more fun demos on our hugging face space:
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fantastic slides on how inference engines work.
zain is an amazing teacher!
100% local audio-to-text transcription CLI
we just updated the demo to use the LFM2.5 family:
serve LFM2.5-Audio-1.5B with llama.cpp on your laptop and let it transcribe any audio input
code:
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we published a blog on hugging face at possibly the worst time yesterday lol
congrats to the HF team on the big news! 💚
here's a fine-tuning tutorial showing how to
• fine-tune a tiny LFM2.5-350M model
• in 100 GRPO steps using TRL
• for better structured outputs
blog:
colab:
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i'm gonna get shredded to pieces like tibo and jarred when they see what i actually look like now...
i'll be speaking at the 2nd edition of @aiDotEngineer paris on sep 24th 🇫🇷
i'll be talking about:
• current state of post-training
• how our team built the first reliable on-device agentic model
• and a demo running a local agent with LFM2.5-2.6B
come say hi (if you can recognize me)
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Which is the best model to run on an iPhone 17 Pro?
Now you'll know:
Our team at Liquid AI has partnered with Artificial Analysis to bring you an open-source benchmark to measure on-device quality, speed, latency, and memory.
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study notes on speculative decoding
The @liquidai cookbook is such an underrated developer resource:
Curious about fine-tuning text, vision, audio, or encoder models?
Curious about fine-tuning with CPT, SFT, DPO, or GRPO?
Curious about fine-tuning LFMs with Unsloth or TRL?
It has it all.
I just did a little cleanup. Enjoy!
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New set of LFM2.5-Encoders just dropped.
These are super fast, even at long context and on CPU.
Our team adapted them from their LFM2.5 backbones:
1. Replaced the causal attention mask with a bidirectional one
2. Made the LFM short convolutions non-causal
3. Trained with a masked language modeling objective
The result is a set of two tiny, super fast encoders.
Encoding a document of 12 to 15 pages (about 8k tokens) takes less than 30s on a CPU.
Release blog:
Models on Hugging Face:
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