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
You might also consider the decision models, d1-2B is very strong and good for calibrated classification. Would be happy to share links/resources if you're interested.
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
Is interesting how the live mode is restarting the text every 30 sec, wonder how it can impact a thing that may be agreed over several minutes.
Say I would like to get action items out of an hour long recording, am I supposed to go straight from the audio with LFM3-Audio-2B? Or is it transcribe first and give the text to LFM3-1.7B?
What if I want to fine-tune the action items routine. How many labelled examples do you estimate such task may need?
It all depends on your hardware and throughput requirements. Suppose
1. I were building for my work laptop (M4 Max, 36GB RAM)
2. I had a batched use cases (transcribing + making action items for all meetings in a day)
I would use the LFM3-Audio-2B for ASR (audio -> text transcript) and then LFM3-1.7B for extracting particular action items and adding them to my tool of choice (Linear, Jira, Notes app, etc).
If you can fine-tune (100+ labeled samples, and a GPU), you likely can fine-tune LFM3-Audio-2B for audio -> action items in a single step.
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...
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.
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.
Agreed not, but I think we are also moving towards a place where harnesses have code mode built in and where data swizelling and tool use is something the LLMs have to be competent at. I think that's present enough here!
And to beat Qwen3.5-4B with such a capable model is amazing, on top of so many win/draws against 9b in the more general arena.
I can definitely see there being some roles for local agents that offload shell scripting & doing some scut work. I don't know what speed to expect but like, if someone has a decent NPU or GPU and can just get a bunch of basic systems work done with a small local model that could really speed up a lot of workflows that I see day after day after day.
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.
How do you determine confidence levels? logprobs?
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
You might also consider the decision models, d1-2B is very strong and good for calibrated classification. Would be happy to share links/resources if you're interested.
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?
0: http://jeffyclassify.com/
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
Have you considered fine-tuning on your dataset?
Seeing the fine-tuning guides that are coming out with LFM3, I am now considering it :)
Would be a fun experiment.
I worked on this release, happy to answer questions/chat about local AI vs cloud frontier models.
Is interesting how the live mode is restarting the text every 30 sec, wonder how it can impact a thing that may be agreed over several minutes. Say I would like to get action items out of an hour long recording, am I supposed to go straight from the audio with LFM3-Audio-2B? Or is it transcribe first and give the text to LFM3-1.7B? What if I want to fine-tune the action items routine. How many labelled examples do you estimate such task may need?
It all depends on your hardware and throughput requirements. Suppose
1. I were building for my work laptop (M4 Max, 36GB RAM)
2. I had a batched use cases (transcribing + making action items for all meetings in a day)
I would use the LFM3-Audio-2B for ASR (audio -> text transcript) and then LFM3-1.7B for extracting particular action items and adding them to my tool of choice (Linear, Jira, Notes app, etc).
If you can fine-tune (100+ labeled samples, and a GPU), you likely can fine-tune LFM3-Audio-2B for audio -> action items in a single step.
I am seeing the following in the reasoning trace:
> 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?
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.
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?
Depends on what you care about.
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.
No weekends. We need you to get back to work on the next 8B.
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)
Congrats for the achievement!
I'm curious if this model would be useful for OCR or at least text documents parsing (PDF, DOCX, etc.), what do you think?
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...
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.
quantization is always a problem; what quant were you using?
What do they say if you ask what cows drink? ;) Actually I’d be curious whether they would justify ‘milk’ with ‘when they’re very young’.
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:
- https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-8bit
- https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-8bit
Looks really neat. I'm keen to try this on my clicks communicator when that arrives.
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.
Post not up yet, but the models are: https://huggingface.co/collections/LiquidAI/lfm3
Title is: Introducing LFM3: Every Modality for Specialized Intelligence
updated, ty
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.
what does your home assistant do? a general siri/alexa-style model? or something else?
He probably means the Home Assistant software at home-assistant.io
Loved playing with LFM2.5 and excited about this!
Agentic coding numbers on par with Qwen3.5-4B, the rest of the numbers on par with Qwen3.5-9B. Most impressive for a 1.7B model!
Liquid has also been very good about keeping speed in mind, and the writeup here makes me expect similar. Really good stuff.
Is anyone going to seriously use a model this small for coding? Doesn't seem like the intended use case or strength of these models.
Agreed not, but I think we are also moving towards a place where harnesses have code mode built in and where data swizelling and tool use is something the LLMs have to be competent at. I think that's present enough here!
And to beat Qwen3.5-4B with such a capable model is amazing, on top of so many win/draws against 9b in the more general arena.
I can definitely see there being some roles for local agents that offload shell scripting & doing some scut work. I don't know what speed to expect but like, if someone has a decent NPU or GPU and can just get a bunch of basic systems work done with a small local model that could really speed up a lot of workflows that I see day after day after day.
has anyone tried finetuning one of these yet?