One thing I have been using LFM 2.6 for is annotating emails, text messages, and other metadata. I keep it all ingesting into one giant database and annotate it all as it comes in on an RTX A4500. The LFM annotations are basically triage, and then slowly annotations trickle into my halo model based on lowest confidence (right now that's DeepSeek 4.1 Flash 1017) for an upgraded annotation. (Agents can then search this database for context).
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.
You're doing something similar to what I've been prototyping for the automatic metadata enrichment part. So far I've been using deterministic older techniques + manual for my personal data stream, but I'm trying to learn what the state of the art is for efficiently annotating data with LLMs and newer ML.
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
That's very cool. I'm curious if you need a GPU for your use case (email and text triaging). There are some open source alternatives[0] that run/train on CPU, so you can self-host or run locally (and train on your own data for better accuracy), would something like that work for you?
I can highly recommend gliner2.5, been using it for tagging news articles, fine tuned on my home 5090 in a few minutes on 500 articles tagged by gpt 6.1, works fantastically. Deployed on hetzner running on a cpu .
gliner-style models are absolutely crazy, I learned about them a couple weeks ago when a co-worker fine-tuned an LFM encoder for promptable classification. as far as I know, you don't need to fine-tune; you can just prompt for classification. My co-worker's demo is here: https://huggingface.co/spaces/LiquidAI/prompt-routing
Surprisingly (or maybe not?), LFM3-VL-2B gave me better results than LFM3-1.7B on text-only prompts (general knowledge, chatting) through liquid playground.
As an example, when asked “what does a cow say?”, LFM3-1.7B said that a cow can say “Oink”, and when I asked the same question to LFM3-VL-3B, it (correctly) said “moo”. I assume this is lower quant problem, though: Q8 versions I’ve played with through llama both give the correct “moo” answer.
Which specific model? and are tools available? I would say that there is lots of RL with tool use, but that the reasoning traces are not always perfect. In general, models think about things that might be wrong, but will recover from this poor line of thinking.
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.
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)
Yes! LFM3-VL-2B is a very strong OCR model for its size, especially given how general a model it is. You can also fine-tune it with a specific output format and it will dramatically improve. We have a Google Colab here: https://colab.research.google.com/github/Liquid4All/cookbook...
Just tried both the Omni model and the text only model, both as mlx versions, in lm studio omlx and rapid mlx, none of them could actually fire the model up, kept saying safetensors cant be read (yes I realize Claude should hear this not this thread).
I think mlx-vlm should support both the Audio and the Omni model: https://github.com/Blaizzy/mlx-vlm/ and there should be native MLX exports for both here:
I’ve been using 2.6B as my main home assistant model and it’s been good. I switched to Ling 3.0 Tiny since it’s faster with A1B. Really hoping 1.7B is good enough (2.5 version was too dumb for HA) so I can run everything on one machine.
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
0: http://jeffyclassify.com/
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.
- https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-8bit
- https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-8bit
Liquid has also been very good about keeping speed in mind, and the writeup here makes me expect similar. Really good stuff.