alt.freeweights.lfm / LFM2.5-VL-450M-ONNX
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.lfmSubject:

LFM2.5-VL-450M-ONNX

Date: 2026-11-15Commit: 24e6300Signed: 2DC28CC7B26A283B ✓

Image text to text model. Onnx weights, 4.46 GB in all. Published by onnx-community under LFM-1.0 (custom).

lfm4.46GBLFM2.5-VL-450M-ONNXonnxcustom terms
FactsParamsnot statedSize4.46 GBFormatsonnxTaskimage text to textLicenseLFM-1.0 (custom)From sourcecustom termsread terms
# custom terms: read the license before you download
$ fw get onnx-community/LFM2.5-VL-450M-ONNX
# manifest sha256 53eebd696f82… · 4.46 GB · 39 files
$ fw verify ./LFM2.5-VL-450M-ONNX onnx-community/LFM2.5-VL-450M-ONNX
From sourceHosted (signed)onnx-community/LFM2.5-VL-450M-ONNXLast check: 2026-11-15 00:22 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

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Overview

Size
4.46 GB
Weights
38.8 MB (smallest of 15 ONNX variants)
Parameters
not stated
Formats
onnx
Task
image text to text
License
LFM-1.0 (custom) (custom)
Files
39, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.lfm
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Original release from the publisher.

Base model: LiquidAI/LFM2.5-VL-450M

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Model card

From the publisher at commit 24e6300. Shown as plain text with links; images open at the publisher.

Image: Liquid AI

Try LFM • Docs • LEAP • Discord

LFM2.5‑VL-450M#

LFM2.5‑VL-450M is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-450M, built on an updated backbone LFM2.5-350M and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our blog post.

  • Enhanced instruction following on vision and language tasks.
  • Improved multilingual vision understanding in Arabic, Chinese, French, German, Japanese, Korean, Portuguese and Spanish.
  • Bounding box prediction and object detection for grounded visual understanding.
  • Function calling support for text-only input.

🎥⚡️ You can try LFM2.5-VL-450M running locally in your browser with our real-time video stream captioning WebGPU demo 🎥⚡️

Alternatively, try the API model on the Playground.

📄 Model details#

LFM2.5-VL-450M is a general-purpose vision-language model with the following features:

  • LM Backbone: LFM2.5-350M
  • Vision encoder: SigLIP2 NaFlex shape‑optimized 86M
  • Context length: 32,768 tokens
  • Vocabulary size: 65,536
  • Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish
  • Native resolution processing: handles images up to 512*512 pixels without upscaling and preserves non-standard aspect ratios without distortion
  • Tiling strategy: splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context
  • Inference-time flexibility: user-tunable maximum image tokens and tile count for speed/quality tradeoff without retraining
  • Generation parameters:
    • text: temperature=0.1, min_p=0.15, repetition_penalty=1.05
    • vision: min_image_tokens=32 max_image_tokens=256, do_image_splitting=True

We recommend using it for general vision-language workloads, captioning and object detection. It’s not well-suited for knowledge-intensive tasks or fine-grained OCR.

Chat Template#

LFM2.5-VL uses a ChatML-like format. See the Chat Template documentation for details.

<|startoftext|><|im_start|>system
You are a helpful multimodal assistant by Liquid AI.<|im_end|>
<|im_start|>user
<image>Describe this image.<|im_end|>
<|im_start|>assistant
This image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|>

You can use processor.apply_chat_template() to format your messages automatically.

🏃 Inference#

You can run LFM2.5-VL-450M with Hugging Face transformers.js v4.2.1 or newer:

npm i @huggingface/transformers

You can then use the model as follows:

import {
  AutoProcessor,
  AutoModelForImageTextToText,
  load_image,
  TextStreamer,
} from "@huggingface/transformers";

// Load processor and model
const model_id = "onnx-community/LFM2.5-VL-450M-ONNX";
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await AutoModelForImageTextToText.from_pretrained(model_id, {
  device: "webgpu",
  dtype: {
    embed_tokens: "fp16",
    decoder_model_merged: "q4f16",
    vision_encoder: "fp16",
  },
});

// processor.image_processor.do_image_splitting = false; // Disable image splitting for this demo (faster)

const messages = [
  {
    role: "user",
    content: [
      { type: "image" },
      { type: "text", text: "Describe this image." },
    ],
  },
];
const prompt = processor.apply_chat_template(messages, {
  add_generation_prompt: true,
});

// Prepare inputs
const url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/artemis.jpeg";
const image = await load_image(url);
const inputs = await processor(image, prompt, { add_special_tokens: false });

const outputs = await model.generate({
  ...inputs,
  max_new_tokens: 2048,
  streamer: new TextStreamer(processor.tokenizer, {
    skip_prompt: true,
    // callback_function: (text) => { /* Do something with the streamed output */ },
  }),
});

// Decode output
const decoded = processor.batch_decode(
  outputs.slice(null, [inputs.input_ids.dims.at(-1), null]),
  { skip_special_tokens: true },
);
console.log(decoded[0]);

See example output

This image captures a dramatic scene of an American flag prominently displayed on a flagpole against a clear, cloudless blue sky. The flag, with its iconic red and white stripes and blue field adorned with white stars, is fluttering in the wind, suggesting a strong breeze. The flagpole, which is black and topped with a spherical finial, supports the flag at an angle, with the flag fluttering to the left.

In the background, a rocket is seen launching into the sky, its nose pointed upwards and emitting a bright, white flame. The rocket's exhaust trails a vivid orange and yellow, creating a striking contrast against the clear blue sky. The image is taken from a low angle, emphasizing the flag's movement and the rocket's ascent, capturing a moment of national pride and technological achievement.

🔧 Fine-tuning#

We recommend fine-tuning LFM2.5-VL-450M model on your use cases to maximize performance.

NotebookDescriptionLink
SFT (Unsloth)Supervised Fine-Tuning with LoRA using Unsloth.Colab link
SFT (TRL)Supervised Fine-Tuning with LoRA using TRL.Colab link

📊 Performance#

LFM2.5-VL-450M improves over LFM2-VL-450M across both vision and language benchmarks, while also adding two new capabilities: bounding box prediction on RefCOCO-M and function calling support measured by BFCLv4.

Vision benchmarks#
ModelMMStarRealWorldQAMMBench (dev en)MMMU (val)POPEMMVetBLINKInfoVQA (val)OCRBenchMM-IFEvalMMMBCountBenchRefCOCO-M
LFM2.5-VL-450M43.0058.4360.9132.6786.9341.1043.9243.0268445.0068.0973.3181.28
LFM2-VL-450M40.8752.0356.2734.4483.7933.8542.6144.5665733.0954.2947.64-
SmolVLM2-500M38.2049.9052.3234.1082.6729.9040.7024.6460911.2746.7961.81-

All vision benchmark scores are obtained using VLMEvalKit. Multilingual scores are based on the average of benchmarks translated by GPT-4.1-mini from English to Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

Language benchmarks#
ModelGPQAMMLU ProIFEvalMulti-IFBFCLv4
LFM2.5-VL-450M25.6619.3261.1634.6321.08
LFM2-VL-450M23.1317.2251.7526.21-
SmolVLM2-500M23.8413.5730.146.82-

📬 Contact#

Citation#

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}

Files

39 files · 4.46 GBWeights 38.8 MB (smallest of 15 ONNX variants). Sizes and sha256 from the signed manifest.
Show all 39 files with their sha256
FileSizesha256
.gitattributes2.60 KB88be53b636fef413936c96a641b732b2c8d5f1b6f7d8b0f10e6a95980de45067
README.md9.55 KBf64f2bc1fac67ff8ae20aa67698e14d842defb3d2c86b5bdd7b085cf41dd5d5f
chat_template.jinja3.84 KB309e586e2eda3d7f2db1e2a045bfb07f4c83798b23f7ac587954426302d508e9
config.json2.64 KB66e60e8a4e140bcd46d09a65342287982131443ce12496bd2478cec2b1d8fd14
generation_config.json136 B90783ccbe100f9169a9cfa588d7bff8819e2de015642f1af13cd46bf06b2a6f1
onnx/decoder_model_merged.onnx143 KBcca5a42c397557bd837a5eadaccffffe8b7a87eb4bc889961f63490dedecc4c3
onnx/decoder_model_merged.onnx_data1.45 GBa10ed1f3b85b1d40e0ff194091cc1120c23d12324519fc738b34b0bd797a9fe2
onnx/decoder_model_merged_fp16.onnx142 KB76244c6020c38a52b76059b5e25dc03d02b238cf33af7530655794362d0cebe1
onnx/decoder_model_merged_fp16.onnx_data725 MB541b73608bb62ad36a01910f35dcff1b88866c1967c5f838e17f1685c1278e9d
onnx/decoder_model_merged_q4.onnx187 KB00b4c0ed1008194b6ed813e5d17724db122ef71e963197424022aaf93966515a
onnx/decoder_model_merged_q4.onnx_data260 MB0440e6e97953a70705ef1901cb1267bc80cb69ae7d4ca25010891c5770e989d5
onnx/decoder_model_merged_q4f16.onnx187 KB7240383efa592695733484b1e4ec4c0474a652d445280c748baf10b51eceacb8
onnx/decoder_model_merged_q4f16.onnx_data221 MBa93e7fc1821e8aaefc33d30af4299411cf0490456d769795bd30db00cb38be95
onnx/decoder_model_merged_quantized.onnx190 KB028a61691aa9ce30d2ff939f396ceead91b46c6d7fcd02a2f49d9e9ff2672f14
onnx/decoder_model_merged_quantized.onnx_data443 MB8391e6e2ee53bebc7a4d4b5cf182930a9c3da155f553391a37f7080824ce3528
onnx/embed_tokens.onnx434 Ba3fdfb2e2e3a0b69a328ab6c674e37e2aeb45b9637c44fa150a51d6ccbc90258
onnx/embed_tokens.onnx_data268 MB9ff7054e7ccb91937cf032fb2fad0b459c168e60fbb379bc4c030ed17ef2165c
onnx/embed_tokens_fp16.onnx637 B4e12bdaca4b895f95576c5dcc68324691cdcc7fd0912f3900809045381f82f79
onnx/embed_tokens_fp16.onnx_data134 MB40f6efc09b9e0e22946dbf749a38017ea8a1a812d7f4b25eea27c9ebb4a4ce39
onnx/embed_tokens_q4.onnx853 Bf0d663cbf75fc6a0c7b9669177335139b0c5a63575c6413037d48501eea0c4a5
onnx/embed_tokens_q4.onnx_data43.0 MB255994cbb7269ea24b43d3d57a7e64dcb54da69c77ea612f9c32af3dbb95158e
onnx/embed_tokens_q4f16.onnx1.06 KB8b0b2f8ce26a383d2064bac1949f0fee763dfc5625efa6b15166f6b115f23836
onnx/embed_tokens_q4f16.onnx_data38.8 MB57b12507e5ad10435ae86ff73a7b2ea47119009d63706f3f843c4653b297152a
onnx/embed_tokens_quantized.onnx875 B42a036e66fb394db463db62d2cba29e45099be5d8f44b6435293d7bfe1cab30e
onnx/embed_tokens_quantized.onnx_data77.6 MBb95888b549a8b207ad370a243e4c8ba742141445e6dd6e36ead9e74a725ab34e
onnx/vision_encoder.onnx150 KBf30485fffe7c0c0365526c5350e068662a67340dd5ea561b329aabf960476a3d
onnx/vision_encoder.onnx_data377 MBeb34cdb051d2c99e6ab0af147a0e7ab29a7db920fa6eba1834c41534870a6367
onnx/vision_encoder_fp16.onnx152 KB359f0344b26ed1fe853a081bac053ed4231854d1ab43c1f5d055ade0be7ba218
onnx/vision_encoder_fp16.onnx_data189 MBb4633153bc838509ded8174e7c5991230217e46bd8ad8b5d19755f654254566d
onnx/vision_encoder_q4.onnx185 KB3457fe118939ecd52183660abafbbd32c810f41a0e8d1119a1f07ca2d4d9dcfc
onnx/vision_encoder_q4.onnx_data61.5 MB03171ff302af006d2e5f55f9c09531d7938565626334809c94e6de54afc840b5
onnx/vision_encoder_q4f16.onnx187 KB3b3c649be161ac04196dccf17a6dacbbc5bba27d305dc76df541da971a04b938
onnx/vision_encoder_q4f16.onnx_data55.3 MB22cafaabfa07020c4426962e2c71aff05fd53b63af971bbbcf094f5cf7c9af07
onnx/vision_encoder_quantized.onnx187 KB6528db8a73519a8c245f1ea011784b2c04e6d4288b05fb5719bf457f66310a9d
onnx/vision_encoder_quantized.onnx_data110 MBe6e5440653518318dbe975ea7d4feabbc64e8a8fc103953c618d96127e1e5539
preprocessor_config.json729 Bc38f1a6b18858bb57cfc087ea36cad473bdf9b9cce46a04ed991a82b1ff024bb
processor_config.json824 Bc70448272e6c85a2bf3f3d55359f07e070b746e9f4c7c6271724d8955fac385b
tokenizer.json3.30 MBd3f7877aa8c9ce603604f2cf78c280c24d8b6087c24669610f3391bcd3f703cf
tokenizer_config.json5.44 KBcafbd63d001d1d7c815a6d588b26963b6ea51dade224959e2f4203dcc1dd17d8
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
24e63007ff pinned now2026-11-15 00:22 UTC39 files · 4.46 GB

Use it

# custom terms: read the license before you download
$ fw get onnx-community/LFM2.5-VL-450M-ONNX
# manifest sha256 53eebd696f82… · 4.46 GB · 39 files
$ fw verify ./LFM2.5-VL-450M-ONNX onnx-community/LFM2.5-VL-450M-ONNX
Terms that come with these files. LFM 1.0: commercial use only for organisations under US$10M annual revenue. Keep a copy of the license with the files.
Custom license (custom). Read the license text first. Open this panel to show the commands once you accept its terms.
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/LFM2.5-VL-450M-ONNX.json --dir ./LFM2.5-VL-450M-ONNX $ python3 fw.py verify ./LFM2.5-VL-450M-ONNX ./onnx-community__LFM2.5-VL-450M-ONNX.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/LFM2.5-VL-450M-ONNX --revision 24e63007ff1aaafbde27c89eb01a0513f4f61482 --local-dir ./LFM2.5-VL-450M-ONNX

License

Name
LFM Open License v1.0
Id
LicenseRef-LFM-1.0
Class
Custom terms
Redistribution
click-through
Listing
Hosted (signed)
Terms
LFM 1.0: commercial use only for organisations under US$10M annual revenue. Keep a copy of the license with the files.
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
f64f2bc1fac67ff8ae20aa67698e14d842defb3d2c86b5bdd7b085cf41dd5d5f
Terms gate
Yes. The commands stay folded until you open them after reading the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.lfmSubject: LFM2.5-VL-450M-ONNXDate: 2026-11-15Commit: 24e6300Signed: 2DC28CC7B26A283B ✓
Upstream
onnx-community/LFM2.5-VL-450M-ONNX
Pinned commit
24e63007ff1aaafbde27c89eb01a0513f4f61482
Manifest
LFM2.5-VL-450M-ONNX.json · .minisig
Manifest sha256
53eebd696f82da0dd297946799669fb0195774abfbba078dfd95299e1d80d1e9
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 00:22 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/LFM2.5-VL-450M-ONNX.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/LFM2.5-VL-450M-ONNX.json.minisig $ minisign -Vm LFM2.5-VL-450M-ONNX.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

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Get it
Read license, then download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/LFM2.5-VL-450M-ONNX
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Signed manifest
minisign, key 2DC28CC7B26A283B. LFM2.5-VL-450M-ONNX.json. Provenance. Takedown history: none.
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-1.2B
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-1.2B-Extract
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-1.2B-RAG
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-1.2B-Tool
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-2.6B
From: LiquidAI <huggingface.co/LiquidAI>Subject: LFM2-2.6B-Exp