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alt.freeweights.phi / Phi-3.5-vision-instruct-onnx
From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.phiSubject:

Phi-3.5-vision-instruct-onnx

Date: 2026-11-15Commit: 672d733Signed: 2DC28CC7B26A283B ✓

Model. Onnx weights, 5.82 GB in all. Published by microsoft under MIT.

phi5.82GBPhi-3.5-vision-instruct-onnxonnxpermissive
FactsParamsnot statedSize5.82 GBFormatsonnxTasknot taggedLicenseMITFrom sourcepermissive
$ fw get microsoft/Phi-3.5-vision-instruct-onnx
# manifest sha256 6e3a0dd4f9fb… · 5.82 GB · 29 files
$ fw verify ./Phi-3.5-vision-instruct-onnx microsoft/Phi-3.5-vision-instruct-onnx
From sourceHosted (signed)microsoft/Phi-3.5-vision-instruct-onnxLast check: 2026-11-15 03:58 UTC
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Overview

Size
5.82 GB
Weights
6.09 KB (smallest of 6 ONNX variants)
Parameters
not stated
Formats
onnx
Task
not tagged
License
MIT (permissive)
Files
29, each with a sha256
Publisher
microsoft
Added
2026-11-15
Section
alt.freeweights.phi
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Model card

From the publisher at commit 672d733. Shown as plain text with links; images open at the publisher.

Phi-3.5 Vision Instruct ONNX models#

This repository hosts the optimized versions of Phi-3.5-vision-instruct to accelerate inference with ONNX Runtime for your CPU and GPU.

Phi-3.5 Vision is a lightweight, state-of-the-art open multimodal model built upon datasets that include synthetic data and filtered publicly available web data with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3.5 model family, and the multimodal version supports up to 128K context length (in tokens). The base model has undergone a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization, to ensure precise instruction adherence and robust safety measures.

Optimized variants of the Phi-3.5 Vision models are published here in ONNX format to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets.

ONNX Models#

Here are some of the optimized configurations we have added:

  1. ONNX model for INT4 CPU: ONNX model for CPUs using int4 quantization via RTN.
  2. ONNX model for INT4 GPU: ONNX model for GPUs using int4 quantization via RTN.

How to Get Started with the Model#

To support the Phi-3.5 models across a range of devices, platforms, and EP backends, we introduce a new API to wrap several aspects of generative AI inferencing. This API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps here.

Hardware Supported#

The models are tested on:

  • Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
  • 1 A100 GPU, SKU: Standard_ND96amsr_A100_v4 (CUDA)
  • GPU SKU: RTX 4080 (DirectML)

Minimum Configuration Required:

  • CPU machine with 16GB RAM
  • CUDA: NVIDIA GPU with Compute Capability >= 7.0
  • Windows: DirectX 12-capable GPU and a minimum of 10GB of combined RAM
Model Description#
  • Developed by: Microsoft
  • Model type: ONNX
  • Language(s) (NLP): Python, C, C++
  • License: MIT
  • Model Description: This is a conversion of the Phi-3.5 Vision Instruct model for ONNX Runtime inference.
  • Disclaimer: This model is only an optimization of the base model. Any risk associated with the model is the responsibility of the user of the model. Please verify and test for your scenarios. There may be a slight difference in output from the base model with the optimizations applied. We have conducted responsible AI evaluations and did not observe significant regressions compared to the base model.

Additional Details#

Performance Metrics#

The performance of the ONNX vision model is similar to Phi-3.5-mini-instruct-onnx during token generation.

Base Model Usage and Considerations#

For details and RAI considerations of the base model, please refer to here.

Please note that ONNX model output may vary slightly from the base model. The users are responsible for verifying the output for their scenarios and own responsibility of the usage.

Appendix#

Model Card Contact#

parinitarahi, kvaishnavi, natke, yunl, sunghcho

Contributors#

Kunal Vaishnavi, Sunghoon Choi, Yufeng Li, Baiju Meswani, Sheetal Arun Kadam, Rui Ren, Natalie Kershaw, Parinita Rahi, Patrice Vignola, Xiang Zhang, Chai Chaoweeraprasit, Logan Iyer, Vicente Rivera, Jacques Van Rhyn, Yun Liu

Trademarks#

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

Files

29 files · 5.82 GBWeights 6.09 KB (smallest of 6 ONNX variants). Sizes and sha256 from the signed manifest.
Show all 29 files with their sha256
FileSizesha256
.gitattributes1.57 KB1403a1a07764f40b81196144fc294c8daff49cbc0f1d0a79fcfbf5e35a027311
LICENSE1.08 KBfa8235e5b48faca34e3ca98cf4f694ef08bd216d28b58071a1f85b1d50cb814d
README.md4.71 KB617344990918b5676fe75e3b9c965129e0af1589c98f35bffb544178933ba0cf
config.json3.78 KB567e28c638a3e41aba1a304ae58b283845ad07e1c60e970643b0ad8fc229b382
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/genai_config.json2.12 KBaab58b61ca85a8bf2d4781fe1b4299e3d0f20a4cd902bc5c02adfe361b62aad9
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-embedding.onnx6.09 KB83bb330b0a319812eccdf6b6bc5ce17eb6a120bcf76120f6af27c19c3d396ea1
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-embedding.onnx.data394 MB2dcbf0fcb14433ac8fa82b35c39675c01fb552512b4c0e5d54e8f247046ef393
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-text.onnx52.1 MBf7580bd589693e87e3825ffe8eda3f5fda4158d2d82d27803c263aa0e7454a81
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-text.onnx.data2.33 GB214fd2e8a09b8c7b141f9095171aed6f994768a36803b7eeb9ea3be16da99dd6
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-vision.onnx414 KBe06ccb2d2c35c2565f0abe507241f33cae9d7f63a1d79f6170b317d12f35c3bf
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/phi-3.5-v-instruct-vision.onnx.data445 MB45af0e626ba0eff702d9c1873c84f9e60f1f17bb2036bf1abc045968b5e645df
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/processor_config.json752 Bb6f5a3ceb16ad076b31430e358d68f0ebb53d9ddeb91270e9e5c4ff8fefed67f
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/special_tokens_map.json670 Bf9535fada19315e0d8eb89521cebe111c008fee98d16e7c6744acfb50505d8c3
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/tokenizer.json1.85 MB478fb6fd0bf1403eb2ffda892e285f5ae968b9e0c06eb2815552ebedacb8ca93
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/tokenizer_config.json9.55 KBd637971d627e4f8ce0422401bd0a305d9f763657ad21e8205afe8d36b6672524
gpu/gpu-int4-rtn-block-32/genai_config.json2.12 KBaab58b61ca85a8bf2d4781fe1b4299e3d0f20a4cd902bc5c02adfe361b62aad9
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-embedding.onnx6.09 KBb707f7f659d8585a9253f3b95b1740522f3062a7eb4465f09d93c984743e399d
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-embedding.onnx.data197 MBe0af51a7c122ed63878262191faa905c1e317c7153185a6f7275e61d22fda63a
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-text.onnx292 KBd598bf368ed0b84b9d9db91a6a4843a2c92d071bf51df6763daadb3213922204
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-text.onnx.data2.12 GBde9df6b7062a3d133af7674041e583534dff5a95758572c15468eb1bf602e576
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-vision.onnx422 KB30d5d0dc644db606e42cf0d759acb2a22aef4ef572c77689e910ad9fa3b5ad1e
gpu/gpu-int4-rtn-block-32/phi-3.5-v-instruct-vision.onnx.data282 MB68aafcb815850a4982644fc81efec6800054865bb6dbc4ff41f275cc73533eac
gpu/gpu-int4-rtn-block-32/processor_config.json752 Bb6f5a3ceb16ad076b31430e358d68f0ebb53d9ddeb91270e9e5c4ff8fefed67f
gpu/gpu-int4-rtn-block-32/special_tokens_map.json706 B8aa9f4bae46e5280787c8979252ffbf81f411a9aae79650f33a03d2a4b282514
gpu/gpu-int4-rtn-block-32/tokenizer.json1.85 MB478fb6fd0bf1403eb2ffda892e285f5ae968b9e0c06eb2815552ebedacb8ca93
gpu/gpu-int4-rtn-block-32/tokenizer_config.json9.96 KB7743c1615083d279af0170c5550dd7f40ea4de275fb3a99a5cb09bbe43e50662
onnx/builder.py8.16 KBcea0ac45270403b6f8e0704f392166413c205a05f079797906839d098e2747c2
onnx/config.json3.77 KB8ad836368078eaa283601dc8aea701dd437b9827226db9d7532ec8a1ee11af44
onnx/modeling_phi3_v.py94.6 KBf8027b8cf042cc48da84d55f496be2c2d76aa185e487eac11f3535be7460b1a0
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
672d73375f pinned now2026-11-15 03:58 UTC29 files · 5.82 GB

Use it

$ fw get microsoft/Phi-3.5-vision-instruct-onnx
# manifest sha256 6e3a0dd4f9fb… · 5.82 GB · 29 files
$ fw verify ./Phi-3.5-vision-instruct-onnx microsoft/Phi-3.5-vision-instruct-onnx
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/microsoft/Phi-3.5-vision-instruct-onnx.json --dir ./Phi-3.5-vision-instruct-onnx $ python3 fw.py verify ./Phi-3.5-vision-instruct-onnx ./microsoft__Phi-3.5-vision-instruct-onnx.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/Phi-3.5-vision-instruct-onnx --revision 672d73375fa86f3d7787e40ac593e33a4f04a055 --local-dir ./Phi-3.5-vision-instruct-onnx
Run at home

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
LICENSE
Text sha256
fa8235e5b48faca34e3ca98cf4f694ef08bd216d28b58071a1f85b1d50cb814d
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Provenance

From: microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.phiSubject: Phi-3.5-vision-instruct-onnxDate: 2026-11-15Commit: 672d733Signed: 2DC28CC7B26A283B ✓
Upstream
microsoft/Phi-3.5-vision-instruct-onnx
Pinned commit
672d73375fa86f3d7787e40ac593e33a4f04a055
Manifest
Phi-3.5-vision-instruct-onnx.json · .minisig
Manifest sha256
6e3a0dd4f9fb28f79e8c034a04585c6195d5cc8b6cf36aff1a3029f1e88d2b23
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 03:58 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/microsoft/Phi-3.5-vision-instruct-onnx.json $ curl -O https://freeweights.allternit.com/manifests/microsoft/Phi-3.5-vision-instruct-onnx.json.minisig $ minisign -Vm Phi-3.5-vision-instruct-onnx.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

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Signed manifest
minisign, key 2DC28CC7B26A283B. Phi-3.5-vision-instruct-onnx.json. Provenance. Takedown history: none.
Source
huggingface.co/microsoft/Phi-3.5-vision-instruct-onnx

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