Excited to roll out LFM 3 to replace it, looks like a big step up. Right now LFM 2.6 gets trustworthy annotations on just over 40% of data points. Just waiting for these to get into LocalAI.
This seems interesting, but if anyone else is also doing a similar thing, I'd appreciate tips on how to approach it. So far, I think I have some small RAG model in a demo or the very small qwen models. I think fine tuning seems interesting too. Some of the decision models applied in layers (like a binary search on a pre-generated corpus) could be fast enough to help too?
idk too many options, i'm sure this is a solved problem lol
Would be a fun experiment.
> However, the instructions say I must use available tools when answering questions.
On "What animal lays the biggest egg?" with no system prompt
I assume this is something baked into the model through lots of RL with tool use?
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?
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.
I'm curious if this model would be useful for OCR or at least text documents parsing (PDF, DOCX, etc.), what do you think?
It's mediatek NPU claims to handle ONNX. That plus the 12GB ram makes the wonder if I can get this phone to be a mini-agent.
Liquid has also been very good about keeping speed in mind, and the writeup here makes me expect similar. Really good stuff.