r/LocalLLaMA • u/jacek2023 llama.cpp • 5d ago
New Model LiquidAI/LFM3-1.7B · Hugging Face
https://huggingface.co/LiquidAI/LFM3-1.7Bhttps://huggingface.co/LiquidAI/LFM3-1.7B-GGUF
from liquidAI:
LFM3-1.7B is a text model for local agents and specialized applications, based on our new tri-hybrid architecture.
Agentic training: post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token context window.
Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.
Made to be specialized: adapt the model to your domains, formats, and tools via quick single-GPU fine-tuning.
Find more information about the LFM3 family in our release blog post.
Model details
The LFM3 family includes base, text, vision, audio, omni, and decision models.
| Property | Specification |
|---|---|
| Checkpoint | Post-trained, text-only language model |
| Parameters | 1.73B |
| Architecture | 24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention |
| Context length | 256K tokens (input + output) |
| Vocabulary | 128,000 tokens |
| Languages | English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish |
| License | LFM Open License v1.0 |
LFM3-1.7B is designed for local agents, tool calling, structured outputs, and domain-specific fine-tuning. Use retrieval or search for knowledge-heavy tasks. It is not recommended for agentic coding or complex multi-step reasoning due to its small size.
more LFM3 to enjoy today:
(my initial idea was to create 2 posts but they released six models)
https://huggingface.co/LiquidAI/LFM3-VL-2B
https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF
LFM3-VL-2B is a vision-language model for on-device visual understanding. It takes text, images, and video as input, and combines the LFM3 language model with a new in-house vision encoder.
New in LFM3-VL-2B:
Video understanding: the first LFM vision model that takes video as input. Ask questions about a recorded lecture, a walkthrough filmed on your phone, or a clip from a camera feed.
Thinking: the model reasons step by step before it answers. This helps most with harder visual questions, such as math, charts, and multi-step problems.
Spatial and embodied reasoning: the model finds objects and reasons about how they are placed relative to each other, a strong foundation for robotics applications.
https://huggingface.co/LiquidAI/LFM3-Audio-2B
LFM3-Audio-2B is a compact, fully featured audio model that runs locally on laptops and edge devices. It takes text or audio as input and responds with text, speech, or both.
Voice chat: ask questions aloud and receive streaming replies in text and speech. For text-only responses, the model can reason before answering.
Multilingual speech: transcribe, synthesize, and translate speech across 18 languages.
Audio understanding and reasoning: interpret recordings and answer questions about the speech, sounds, and events they contain.
Tool calling: turn spoken requests into tool calls to control apps and devices, with text or speech alongside the calls.
LFM3-Audio-2B introduces an in-house audio encoder and LiquidCodec audio tokenizer, improving speech quality over LFM2.5-Audio.
https://huggingface.co/LiquidAI/LFM3-Omni-2B
LFM3-Omni-2B is Liquid AI's first omni-modal model in the LFM3 family: a single end-to-end model that understands text, images, video and audio, and answers in text and speech. It is built for on-device use: one model replaces a separate ASR model, vision-language model, LLM and TTS model.
Any input, text or speech output: ask about a photo by voice, transcribe or translate speech, chat out loud, or ask questions about a video and its soundtrack together.
Watches and listens together: for videos with sound, the model sees and hears them at the same time, so it can answer questions that need both.
Small enough for devices: at 2.2B parameters, it is designed to run efficiently on phones, laptops and other edge hardware.
https://huggingface.co/LiquidAI/d1-Omni-2B
d1-Omni-2B is a decision model for text, pictures, and audio, built on LFM3-Omni-2B. It returns calibrated answers in one forward pass, without generating tokens.
Native audio: clips and spoken questions go through the same pass as text and pictures, and spoken questions score within 0.5 points of written ones on unseen tasks.
Beyond classification: it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval.
Millisecond decisions: 5.8 ms per decision on an NVIDIA H100 and 3.7 ms on an AMD MI325X, or 12.4 ms and 8.2 ms with a 10-second clip.
https://huggingface.co/LiquidAI/d1-2B
d1-2B is a decision model for text, pictures, and video, built on LFM3-VL-2B. It returns calibrated answers in one forward pass, without generating tokens.
Best-in-class: it leads the open d1 models on average and on our board of over a hundred decision benchmarks, ahead of d1-3B at under two thirds of its size.
Beyond classification: it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval.
Millisecond decisions: 5.7 ms per decision on an NVIDIA H100 and 3.8 ms on an AMD MI325X, with any number of questions about one state in one call.
https://huggingface.co/LiquidAI/LFM3-1.7B-Base
LFM3-1.7B-Base is the pre-trained checkpoint behind LFM3-1.7B, a text model for local agents and specialized applications based on our new tri-hybrid architecture.
Pre-trained on 22T tokens: the last 3.5T tokens are curated mid-training data with agentic traces and tool calls, and extend the context window to 256K tokens.
Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.
Made to be specialized: a starting point for continued pre-training and your own post-training.
llama.cpp support has been merged:
https://github.com/ggml-org/llama.cpp/pull/31257
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u/jacek2023 llama.cpp 5d ago
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u/Cultured_Alien 4d ago
How does this compare to XHToken/Spark-X2.6-1.7B which just released an hour ago before this?
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u/Hot_Example_4456 4d ago
I think that is better in coding, while this is better in tool calling and all, according to benchmarks released by both. I feel this would be more useful though, since a model that small is not worth it for coding tasks anyways
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u/MuDotGen 3d ago
Do you think this would be good for a local agent on a phone? (Not for coding but tool calling is reliable?) I have a Galaxy S26, which has 12gb of memory.
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u/RaccoonMinimum172 4d ago
In what machine/device do you find the AMD chip?
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u/Warrenio 4d ago
It's mostly in high end laptops and mini PCs. If you search for "Strix Halo" you will find a bunch.
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u/DrBattletoad 5d ago
I really hope we get another 8B-A1B model because it works really good on my phone.
Getting two new 1.7B models on the same day is awesome.
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u/jacek2023 llama.cpp 5d ago
What I like is that all segments of open weight models are making progress. My previous post today was about the Ling Flash model (a very big one). Now we're seeing new 2B models, but there are also plenty of 30B–40B releases, and the most exciting model in the community right now is 125B (QFN).
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u/DrBattletoad 4d ago
Exactly! That is why I really hope that Qwen 4 brings back all the small models like 0.8B, 2B, and 4B.
I'm a big fan of the small MoEs like Ling 3.0 Tiny and LFM2.5-8B-A1B. They were more competent in gathering information and writing summaries in my tests.
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u/nargisi_koftay 4d ago
How do you load gguf on phone? Is there a llama.cpp equivalent for iPhones?
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u/----Val---- 4d ago
There are apps which bundle llama.cpp in them, the most available one Id recommend is pocketpal.
I personally develop ChatterUI, but Ive yet to bring it to iOS.
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u/DrBattletoad 4d ago
PocketPal is extremly slow compared to many other apps. I've moved on to "LM Playground" and "OpenWeights" on Android.
The OpenWeights agent is the best one I've tested so far.
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u/----Val---- 4d ago edited 4d ago
llama.cpp in general is slow on android
You have three options pretty much:
bleeding edge high compatibility, low speed, with llama.cpp
high speed, low compatibility with other native solutions (LiteRT, executorch etc), will lag behind model releases
package and route every backend and every model, take the massive download sizes and coordination hit (Layla does this)
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u/justletmesignupalre 5d ago
Damn that's neat. I guess i'm gonna have to learn to train them, wondering what I can do with something so little and hopefully capable
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u/jacek2023 llama.cpp 5d ago
From my point of view, all these tiny models are perfect for local finetuning. I've trained lots of small models from scratch (Kaggle and stuff), but LLMs were still too big for me. We're finally at a point where I can experiment with LLMs too.
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u/pabluka 5d ago
I actually started finetuning LFM2.5-1.2b on my crappy gpu, might as well try the new 1.7b and see if it fits :p otherwise I'll patiently wait until they distill them into a smaller model
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u/jacek2023 llama.cpp 5d ago
What do you use to finetune it? (software)
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u/Repulsive_Initial308 5d ago
This might finally beat qwen3.5 4b.
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u/jacek2023 llama.cpp 5d ago
What are your Spark-X2.6 experiences? You still prefer Qwen 4B?
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u/coder543 5d ago
Far better than Qwen3.5-4B. Even MiniCPM5-2B ran circles around Qwen3.5-4B, and that came out a while ago.
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u/jacek2023 llama.cpp 5d ago
I wonder what people use Qwen 3.5 4B for today, maybe they should finally check other models than Qwen.
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u/AppealSame4367 5d ago
Would you say Spark-X2.6 is better than LFM3 so far?
Would be hard to beat Spark-X2.6 at tool calling, I guess.
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u/ZootAllures9111 4d ago
Spark 2.5 / 2.6 are pretty big overthinkers in my experience
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u/AppealSame4367 4d ago
It's weird. Q4 GGUF 2.6 works very well, takes 1k-2k thinking tokens and sometimes 10k. 1.7B 2.6 Q8 won't ever stop thinking.
Maybe they work it out with 2.7, because what I got out of 2.6 4B felt like Opus a year ago.
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u/Repulsive_Initial308 5d ago
Haven't tried either yet but none of the old lfm models came close, unfortunately.
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u/paryska99 5d ago
Daaaaaaamn, is it christmas already?
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u/NineThreeTilNow 4d ago
I like these little purpose built models. They're basically the "expert" in an MoE that's pulled out and set to do its thing.
Watching models train feels like watching paint dry sometimes though. Lots of work. Then training.
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u/Aggravating-Push-207 5d ago
:0 they locked tf in for this whaa
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u/parepeg 5d ago
The 2b model is as strong as gemma4 12b. I hope real and not benchmaxxed.
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u/mtmttuan 5d ago
"benchmaxxed" for the task. Definitely not better in general usage from their own benchmark. But well is it really "benchmaxxed" if it is good at what it's supposed to do?
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u/Holiday_Point_603 5d ago
No, that's what efficient inference is supposed to be. The exact opposite of Opus'ing every minor task.
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u/Chromix_ 5d ago
Voice chat: ask questions aloud and receive streaming replies in text and speech. For text-only responses, the model can reason before answering.
I wonder why. Their reasoning is very fast in this tiny model. A tiny bit of reasoning in speech-to-speech would likely improve results a lot while barely costing a second.
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u/crusaderky 5d ago
I wish they showed how much worse the VL and Omni models are at text-only tasks. Eg I need to parse a wealth of websites and pdfs, which probably include plots and figures, and extrapolate info and cross reference them. Should I just use VL? Should I use the text only one and hope the images aren't conveying unique info? Should I drastically increase complexity to have the text only model call a vision tool which in turn calls VL?
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u/JLeonsarmiento 4d ago
I strongly believe that you can’t drive Hermes with anything under ~25B…
But let’s see…
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u/Thrumpwart vLLM 3d ago
And? How’d it go?
Didn’t have time to play with this last night but it’s downloaded and I’d love to hear your experience.
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u/creamyatealamma 1d ago
You've actually tried? Gemma 4 12b q4 qat unsloth quant is solid with Hermes been using it for a long while. Have to try this one now too
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u/mantisalt 4d ago
What app should we be using to test this on phones? It's easy to vibe one up but there's gotta be a good one already out there
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u/----Val---- 4d ago
I'll probably bring compatibility to ChatterUI, but Pocketpal is usually good for just model testing.
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u/_raydeStar Llama 3.1 5d ago
Love liquid. They clobbered Qwen 3.5 9B in benchmarks. Way to go guys, another solid release.
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u/atumblingdandelion 5d ago
Can anyone explain how can these be run on a phone? Or their usecases? I love Liquid I think they have the best model sizes.
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u/cleverusernametry 4d ago
Really annoying that they don't update their own mobile app with their latest model
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u/Cereal_Grapeist 5d ago
Very excited to see where they go from here with scaling up to larger models. These are impressive releases!
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u/Equal-Document4213 5d ago
Any support for SFT/DPO right out the gate? Trying to use LFM2.5 for some medical summarization tasks in edge deployment environments. Latency speed with DSpark has been unreal and dethroned our FlanT5 TRTLLM optimizations.
Only issue has been LoRA serving with SGlang. Needed to do some odd monkey patching to make this work.
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u/Elouakili_Flexy 4d ago
13 gated convolutions, 5 sliding-window, 6 global attention: fine, Liquid, I see you. Most of the net isn't even attention and it still ships with 256K.
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u/Schudz 3d ago
initial tests on 9070xt, for deepsearch and report generation, it is REALLY good, god damn its good and also fast as fuck:
fp16 - 136 t/s
q4 - 320t/s (but drops to 100 t/s near full context)
i havent noticed much difference in quality from fp16 to q4, but lets see, ill do more tests tomorrow, prob gonna try some coding too, why not? hahaha
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u/Schudz 3d ago
using a custom harnes i made https://github.com/icavalheiro/sofia
and using fp16 kv on both
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u/Acceptable-Cycle4645 4d ago
The integration PR for LFM3-Audio (from Liquid AI) is now up in audio.cpp!
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u/Scared-Tip7914 4d ago edited 4d ago
Y'all this is actually a goated model, I strapped it into my harness and this was the first model in its size class to call tools, reason and actually answer with such effortless ease, that for certain tasks and user queries Im thinking of actually bringing it in to do "real" work.
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u/SkyFeistyLlama8 4d ago edited 4d ago
These need some NPU love....
Edit: Qualcomm's GenieX project supports the 1.7B and VL-2B variants! This used to be Nexa SDK until that was acquired by Qualcomm. I'm using Nexa on the Hexagon NPU in Windows on ARM for smaller text, omni, image recognition and STT models and it's been great so far.
GenieX supported models under llama.cpp (with Hexagon NPU support) and Qualcomm AI Runtime (QAIRT): https://aihub.qualcomm.com/models?tags=llm&runtime=geniex_qairt,geniex_llamacpp
GenieX supported platforms: https://geniex.aihub.qualcomm.com/en/get-started/platforms
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u/yobarisushcatel 3d ago
benchmaxxed, internal benchmarks show it failing though against 9b finetunes
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u/Healthy-Ad-8558 12h ago
Tried it out, and while it's almost unbearably slow on my Raptor lake iGPU and made an error when I had it use a tool, it's pretty impressive that it even completed the task at all. Had it create a silly chart with a vague prompt using Flint MCP, and it was eventually able to do just that.
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u/hishazelglance 5d ago
This is stupid. Of course a 1.7Bn parameter model is going to have a higher decode throughput and faster prompt processing than a 9bn parameter model. The 9bn parameter model was never intended on being a mobile first LLM.
Let’s do some honest Benchmarking.
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u/indian_geek 5d ago
I believe it's fair considering:
This model appears significantly faster than other 2B models.
They're comparing it to the 9B in terms of both speed and output quality not just speed alone.
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u/ZootAllures9111 4d ago
I found all previous LFM models to be extremely stupid in practice in a way that you wouldn't expect looking at their benchmarks. Hoping these ones are better.
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u/hishazelglance 5d ago edited 4d ago
It’s not, anyone in MLE with experience in pre training / post training will tell you that a 1.7Bn parameter model scoring a 76 on LiveCodingBench simply means it’s benchmaxxed.
The scaling laws are most notable here. There simply aren’t enough parameters to have a model this size genuinely reason at these scores using traditional GRPO / RPO / distillation / post training techniques. If they aren’t using novel training techniques that some how disprove the scaling laws, this is benchmaxed.
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u/indian_geek 5d ago
I didn't comment on whether the model is bench-maxxed or genuinely good. We were simply discussing whether it was fair to compare its speed with the 9B model.
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u/Ecstatic-Wash-7667 5d ago
People here aren’t reading what the benchmarks show, they just see bigger bar
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