points by hypfer 2 days ago | 0 comments
In a few words: Why should I care about this?
What makes it cool? What should make me go "oh yeah let's go"?
From what I can see by skimming the document, I would assume "something something specialized use-cases", but what does that mean? And what does it mean assuming I have attention deficit?
[–] samstevens 2 days ago
[–] colingauvin 2 days ago
No weekends. We need you to get back to work on the next 8B.
[–] 0xbadcafebee 2 days ago
It's a model designed to be tiny and still functional, unlike other larger models that are just scaled down and then run into problems (doom loops, intelligence/reasoning failure, tool call failure). You use it when you want a fine-tuned tiny model processing text/audio/video on multiple platforms. (I don't work for them but this is my takeaway)
[–] liquid_richie 2 days ago
Disclosure: I work at Liquid. For local agents, LFM3-1.7B fits in about 1 GB at 4-bit, holds 256K context, and was post-trained for harnesses like Hermes Agent and OpenClaw. On WebArena-Verified it goes from 3.0 with LFM2.5-2.6B to 38.4. You can fine-tune it for your own task on one GPU.
If you want to do local classification/image processing, I think the d1-2B is amazing, it can run on 1GB of unified RAM.
Now that it's released and I have weekends off again, I'm going to play with the VL-2B model and the d1-2B and see if I can prompt it to control a small Lego robot arm like this https://bricksrl.github.io/ProjectPage/
If you like agents, I think the 1.7B is very strong for its size and can be used to automate small stuff like classifying emails, cleaning your downloads folder, etc.
It can also be fine-tuned for specific use cases with SFT/DPO/GRPO. The blog post talks about fine-tuning for wind-turbine classification. If there's things you want to automate, but GPT-6-Luna is not strong enough, and bigger models are too expensive, you might fine-tune one of the LFM3 models and use it locally for repetitive tasks.