alt.freeweights.text / Florence-2-large
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.textSubject:

Florence-2-large

Date: 2026-11-15Commit: b775129Signed: 2DC28CC7B26A283B ✓

Image text to text model. Onnx weights, 14.8 GB in all. Published by onnx-community under MIT.

text14.8GBFlorence-2-largeonnxpermissive
FactsParamsnot statedSize14.8 GBFormatsonnxTaskimage text to textLicenseMITFrom sourcepermissive
$ fw get onnx-community/Florence-2-large
# manifest sha256 4c66ba0d0bed… · 14.8 GB · 60 files
$ fw verify ./Florence-2-large onnx-community/Florence-2-large
From sourceHosted (signed)onnx-community/Florence-2-largeLast check: 2026-11-15 05:20 UTC

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

Quantized of Florence-2-large.

Overview

Size
14.8 GB
Weights
52.5 MB (smallest of 48 ONNX variants)
Parameters
not stated
Formats
onnx
Task
image text to text
License
MIT (permissive)
Files
60, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.text
Lineage

Original release from the publisher.

Base model: microsoft/Florence-2-large

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 05:20 UTCpassed

Model card

From the publisher at commit b775129. Shown as plain text with links; images open at the publisher.

https://huggingface.co/microsoft/Florence-2-large with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)#

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

Example: Perform image captioning with onnx-community/Florence-2-large.

import {
    Florence2ForConditionalGeneration,
    AutoProcessor,
    load_image,
} from '@huggingface/transformers';

// Load model, processor, and tokenizer
const model_id = 'onnx-community/Florence-2-large';
const model = await Florence2ForConditionalGeneration.from_pretrained(model_id, {
    dtype: {
        embed_tokens: 'fp16', // or 'fp32'
        vision_encoder: 'fp16', // or 'fp32'
        encoder_model: 'q4',
        decoder_model_merged: 'q4',
    },
});
const processor = await AutoProcessor.from_pretrained(model_id);

// Load image and prepare vision inputs
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg';
const image = await load_image(url);

// Specify task and prepare text inputs
const task = '<MORE_DETAILED_CAPTION>';
const prompts = processor.construct_prompts(task);

// Pre-process the image and text inputs
const inputs = await processor(image, prompts);

// Generate text
const generated_ids = await model.generate({
    ...inputs,
    max_new_tokens: 256,
});

// Decode generated text
const generated_text = processor.batch_decode(generated_ids, { skip_special_tokens: false })[0];

// Post-process the generated text
const result = processor.post_process_generation(generated_text, task, image.size);
console.log(result);
// { '<MORE_DETAILED_CAPTION>': 'The image shows a vintage Volkswagen Beetle car parked on a cobblestone street in front of a yellow building with two wooden doors. The car is a bright turquoise color and has a classic design with a round body and a sloping roofline. It has two doors on either side of the car, one on the left side and one in the center, with a brown door on the right side. The doors are made of wood and have a rustic, weathered look. The building behind the car is painted in a light yellow color and appears to be old and dilapidated. The sky is blue and there are trees in the background. The image is taken from a low angle, looking up at the car and the building.' }

We also released an online demo, which you can try yourself: https://huggingface.co/spaces/Xenova/florence2-webgpu


Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Files

60 files · 14.8 GBWeights 52.5 MB (smallest of 48 ONNX variants). Sizes and sha256 from the signed manifest.
Show all 60 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md3.31 KBccf1656596258b73ca150996042182cbe9efc54ad244a9251e9b20d5298cb30b
added_tokens.json22.4 KBba67457c577895525996e7a42d7da60a05856506ea4fd25ff9a023a64662f74b
config.json5.46 KBf745d130340ff48e812147d4aa45140276772b37d77dd23b55941aaf3f4d5f0d
generation_config.json62 B6257874d1d2fd60a5c0786253005d9dfa46f5287903fdb079ca9829ec13cf8b4
merges.txt456 KB1ce1664773c50f3e0cc8842619a93edc4624525b728b188a9e0be33b7726adc5
onnx/decoder_model.onnx1.02 GB9495f968bcf305aa1bd512c6e7850ae9d16b1783b5781394b315f6ba781b1021
onnx/decoder_model_bnb4.onnx149 MBff0b9cfbee73984dfc10833ef5be99a6485f15ecac770f119997b98c75da39ed
onnx/decoder_model_fp16.onnx511 MB059a04344350e80eec87b7a360f77d6d9de27817ebd7ed109f004469b043151f
onnx/decoder_model_int8.onnx257 MB6f0f6cd83c057df5da5a0586d3c5483d9135d17537ec876bb8e8ebbf42627f7e
onnx/decoder_model_merged.onnx1.02 GBb8cc87fea29465237fc9980ee9cf4ef5c6a3e197d78ae6fd1e0b31c20bf7fd80
onnx/decoder_model_merged_bnb4.onnx149 MB96e701c77fc34737334ed853c07365b2329fe7a2b6a80388b51158e7471e38f0
onnx/decoder_model_merged_fp16.onnx512 MB3f007b050621cd1b60392f25a4fe837fb2eda425c11580edd376acc77a369b9b
onnx/decoder_model_merged_int8.onnx258 MB9a87417445e9ecbf0adb68ac0c2c86a7a05f7415b41a45404152528c2dc5d494
onnx/decoder_model_merged_q4.onnx165 MBd13b29ac7da5a998c73d9d8bf9e9380425dc5d03398a82507831651e1a47a374
onnx/decoder_model_merged_q4f16.onnx147 MBceef792245e41918dd2fb0aa7b2a6fb4302bec8c7341cf089a4d3ecbc6d5915d
onnx/decoder_model_merged_quantized.onnx258 MB9a87417445e9ecbf0adb68ac0c2c86a7a05f7415b41a45404152528c2dc5d494
onnx/decoder_model_merged_uint8.onnx258 MB68d47062b82f1ae06eeab32445b25b77c07fc4ee97ae636b7fba6185044df6ea
onnx/decoder_model_q4.onnx165 MB83cb8cead83f91d5c5e4c1ac6a3e20073f96cc9512a6a032c70c2eb704dc21e0
onnx/decoder_model_q4f16.onnx146 MBa8145a13ee4d40712949a5202f63af0d9298f34c9db5b917b25be0be2f95967c
onnx/decoder_model_quantized.onnx257 MB6f0f6cd83c057df5da5a0586d3c5483d9135d17537ec876bb8e8ebbf42627f7e
onnx/decoder_model_uint8.onnx257 MB70eed266a19fd4e2d981209232da882431bc2b378cea0921d8db1ac821a53d55
onnx/decoder_with_past_model.onnx920 MBaf7b1f99422c1aa2e89c3103693d922049c5fbcd9e8a8330b0087c27b6a690a0
onnx/decoder_with_past_model_bnb4.onnx134 MB6c8c17a4be4f6ebb98907194424c77733427441c120065fd4e0ca564a2126be0
onnx/decoder_with_past_model_fp16.onnx460 MB7675346298bd4d5a2c3951346e4aa896c2f5f84d28d07fab261291f20d444505
onnx/decoder_with_past_model_int8.onnx231 MB269740e47586ab594ba6e5f36b3582e18394fde8b04846bd2e6a3bc4be21d2dd
onnx/decoder_with_past_model_q4.onnx149 MB9a39fca9b7151371ba1f8f78f6e531abfd1cbf9521d5f9597027185ade16ea57
onnx/decoder_with_past_model_q4f16.onnx132 MB2461161368e8eaed4809c7d0c443480250d5f2d058b83af38877bdf8993e9c44
onnx/decoder_with_past_model_quantized.onnx231 MB269740e47586ab594ba6e5f36b3582e18394fde8b04846bd2e6a3bc4be21d2dd
onnx/decoder_with_past_model_uint8.onnx231 MB537ce27eb955ec77e1ca54476a75c3fd82c602dcc52d554aeffc78a6e03b3e24
onnx/embed_tokens.onnx210 MBaf0a7ec8918e556c4f3b7931271b8cf882db2024e2b777955daa053ee6a881b3
onnx/embed_tokens_bnb4.onnx210 MB8e2d2d452503b2c75683f21e96659a13b3c3da1561ca59a5294ee35f9f49b78c
onnx/embed_tokens_fp16.onnx105 MB661859f77f1b3f9d2bcdd8eb4fc6e611f48c51abdc55af67de1aa2e0e466ccda
onnx/embed_tokens_int8.onnx52.5 MB8f64649977559f0f88c5beed562d1f4c1f946f4831d17afe433f2d76a7da2fb6
onnx/embed_tokens_q4.onnx210 MB8e2d2d452503b2c75683f21e96659a13b3c3da1561ca59a5294ee35f9f49b78c
onnx/embed_tokens_q4f16.onnx105 MB3677413496147237becbe39cbdb6010f5f6d6ad5f0a0bd3da0746405d2d0301e
onnx/embed_tokens_quantized.onnx52.5 MB8f64649977559f0f88c5beed562d1f4c1f946f4831d17afe433f2d76a7da2fb6
onnx/embed_tokens_uint8.onnx52.5 MB70516a87d14ce774a15ca5ec0d8c2bc58c44552959e372196b741bfaa1c3eb70
onnx/encoder_model.onnx609 MB3046c1c8abee3c1d5ed667db738035a4abbe2793c5298683a8ccf1d6db5f4170
onnx/encoder_model_bnb4.onnx90.0 MBe7a71f51c0be8ff2de5bdb19e1c76344d2657c9cc3e0fd29c7280feb7ace1df8
onnx/encoder_model_fp16.onnx305 MB53211b7c6fd8dc63422d7423fe637fc7debea665996c08a31352943c2e96b231
onnx/encoder_model_int8.onnx153 MB10f49539850b62d21b80f1d0950359aa6b5e6801378fa8aa968a5eaf2f449448
onnx/encoder_model_q4.onnx99.5 MBaa3fdccdbab224adcfbf3c5df7a95ea89cdffc7e917bac9f8a7879b94560c1f0
onnx/encoder_model_q4f16.onnx87.6 MB15deb88e3850d01e3a5ff2056c503426bbeb1adf6aa9a7cd9315153ac239cd09
onnx/encoder_model_quantized.onnx153 MB10f49539850b62d21b80f1d0950359aa6b5e6801378fa8aa968a5eaf2f449448
onnx/encoder_model_uint8.onnx153 MB6eb30cf8a1ff4af6e79b9858494331a122ac919f7b430bbe41c7f6e22d75d8a2
onnx/vision_encoder.onnx1.45 GB3579b2a9fe0e73d80a6e6152c610c80fed3739bc9e386145740ac41a0ac0dc05
onnx/vision_encoder_bnb4.onnx295 MBde1109da0797b3fc5b12858b5670179bcd2aaabfe21053c4baee39abf6221f72
onnx/vision_encoder_fp16.onnx727 MB745a2294f62f935288a8832a344e8d4ebd5133ffbb55284a4784ab96846a4940
onnx/vision_encoder_int8.onnx366 MB579c90e73ce8de0cf11edfb527795c4958a11d733b11aa991d0fb057aa157163
onnx/vision_encoder_q4.onnx316 MB34dff602f09ee6dd50a075df84d0030400d9989bd4d603f5759e2d1c4a576408
onnx/vision_encoder_q4f16.onnx243 MB9aa74cef10bfe9caeef7d1f8420356f9120f75354348ab2f5292004813662d09
onnx/vision_encoder_quantized.onnx366 MB010d201de5488c8e0efb871357dc2e8ae7d372bf756d08419db6ae970ffe033e
onnx/vision_encoder_uint8.onnx366 MB010d201de5488c8e0efb871357dc2e8ae7d372bf756d08419db6ae970ffe033e
preprocessor_config.json2.67 KB3adbbeec44c2b8f41a1e97b3ab2197227fb24be1fc1779c9d0d925f4e77d6559
processor_config.json130 B6eb1a2e487fa8b7521691a3382b5afd92a78e8ea34b385f8824fa07563e92245
special_tokens_map.json147 KB72ff172dc769bc1551b1b4211628ce3271643bc60379e4da45d85a9be9332c39
tokenizer.json2.30 MBd69dcdb2323e124ac4f800cb9863ddccea0d7bb11e16125e8df3bd60f2f8aeac
tokenizer_config.json198 KBd8e64607233cb53b619fb46664f6cad08176c26e0e8735b2d30d888364f19600
vocab.json798 KBed19656ea1707df69134c4af35c8ceda2cc9860bf2c3495026153a133670ab5e
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
b775129592 pinned now2026-11-15 05:20 UTC60 files · 14.8 GB

Use it

$ fw get onnx-community/Florence-2-large
# manifest sha256 4c66ba0d0bed… · 14.8 GB · 60 files
$ fw verify ./Florence-2-large onnx-community/Florence-2-large
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/Florence-2-large.json --dir ./Florence-2-large $ python3 fw.py verify ./Florence-2-large ./onnx-community__Florence-2-large.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/Florence-2-large --revision b7751295927207b2a8c2f354af8baadea5b0404b --local-dir ./Florence-2-large

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
ccf1656596258b73ca150996042182cbe9efc54ad244a9251e9b20d5298cb30b
Terms gate
No extra terms beyond 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.textSubject: Florence-2-largeDate: 2026-11-15Commit: b775129Signed: 2DC28CC7B26A283B ✓
Upstream
onnx-community/Florence-2-large
Pinned commit
b7751295927207b2a8c2f354af8baadea5b0404b
Manifest
Florence-2-large.json · .minisig
Manifest sha256
4c66ba0d0bedc4be229a96a6cec0e0345d5689b6aa68f26351cf29048c267c63
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:20 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/Florence-2-large.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/Florence-2-large.json.minisig $ minisign -Vm Florence-2-large.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

Model tree

Parent

microsoft/Florence-2-large · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/Florence-2-large
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. Florence-2-large.json. Provenance. Takedown history: none.
From: laion <huggingface.co/laion>Subject: a2-rl-e2egit_large-50-32B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-large-15-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-v2-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_pymethods2test-large-80-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_unitsyn-python-large-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_mix_h2_language_proportional-65-8B