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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