alt.freeweights.phi / Phi-4-mini-instruct-onnx

From:

microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.phiSubject:

Phi-4-mini-instruct-onnx

Date: 2026-11-15Commit: fc04c8fSigned: 2DC28CC7B26A283B ✓

Model. Onnx weights, 8.37 GB in all. Published by microsoft under MIT.

phi

8.37GBPhi-4-mini-instruct-onnxonnxpermissive

FactsParamsnot statedSize8.37 GBFormatsonnxTasknot taggedLicenseMITFrom sourcepermissive

$ fw get microsoft/Phi-4-mini-instruct-onnx
# manifest sha256 494297e8ecfc… · 8.37 GB · 27 files
$ fw verify ./Phi-4-mini-instruct-onnx microsoft/Phi-4-mini-instruct-onnx

From sourceHosted (signed)microsoft/Phi-4-mini-instruct-onnxLast check: 2026-11-15 03:45 UTC

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Overview Model card Files Use it License Provenance Model tree

Overview

Size: 8.37 GB
Weights: 287 KB (smallest of 2 ONNX variants)
Parameters: not stated
Formats: onnx
Task: not tagged
License: MIT (permissive)
Files: 27, each with a sha256
Publisher: microsoft
Added: 2026-11-15
Section: alt.freeweights.phi

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Original release from the publisher.

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Sources

Badge	Host	Last check	Result
From source	huggingface.co	2026-11-15 03:45 UTC	passed

Model card

From the publisher at commit fc04c8f. Shown as plain text with links; images open at the publisher.

Phi-4-Mini-Instruct ONNX models#

Introduction#

This repository hosts the optimized versions of the Phi-4 mini models to accelerate inference with ONNX Runtime.

Optimized 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.

Here are some of the optimized configurations we have added:

ONNX model for int4 CPU: ONNX model for CPU and mobile using int4 quantization via RTN.
ONNX model for int4 GPU: ONNX model for GPU using int4 quantization via RTN.

Model Run#

You can see how to run examples with ORT GenAI here

For CPU:

# Download the model directly using the Hugging Face CLI
huggingface-cli download microsoft/Phi-4-mini-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .

# Install the CPU package of ONNX Runtime GenAI
pip install --pre onnxruntime-genai

# Please adjust the model directory (-m) accordingly
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/common.py -o common.py
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/model-qa.py -o model-qa.py
python model-qa.py -m cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4 -e cpu

For CUDA:

# Download the model directly using the Hugging Face CLI
huggingface-cli download microsoft/Phi-4-mini-instruct-onnx --include gpu/* --local-dir .

# Install the CUDA package of ONNX Runtime GenAI
pip install --pre onnxruntime-genai-cuda

# Please adjust the model directory (-m) accordingly
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/common.py -o common.py
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/model-qa.py -o model-qa.py
python model-qa.py -m gpu/gpu-int4-rtn-block-32 -e cuda

For DirectML:

# Download the model directly using the Hugging Face CLI
huggingface-cli download microsoft/Phi-4-mini-instruct-onnx --include gpu/* --local-dir .

# Install the DML package of ONNX Runtime GenAI
pip install --pre onnxruntime-genai-directml

# Please adjust the model directory (-m) accordingly
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/common.py -o common.py
curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/model-qa.py -o model-qa.py
python model-qa.py -m gpu/gpu-int4-rtn-block-32 -e dml

Model Description#

Developed by: Microsoft
Model type: ONNX
License: MIT
Model Description: This is a conversion of the Phi-4 mini model for ONNX Runtime inference.

Disclaimer: 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.

Base Model#

Phi-4-Mini is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures. See details at https://huggingface.co/microsoft/Phi-4-mini-instruct/blob/main/README.md

Performance Comparison#

Hardware	ONNX	PyTorch	speedup
RTX 4090 GPU	int4: 260.045 tokens/sec fp16: 97.463 tokens/se fp32: 19.320 tokens/sec	fp16: 43.957 tokens/sec	5x(fp16)
Intel Xeon Platinum 8272CL CPU	int4: 16.89 tokens/sec	fp32: 1.636 tokens/sec	10x
Intel Xeon Platinum 8573B CPU	int4: 23.978 tokens/sec	fp32: 4.479 tokens/sec	5.35X
AMD EPYC 7763v CPU	int4: 19.884 tokens/sec	fp32: 1.599 tokens/sec	12.4x
Intel Core Ultra 7 165H Laptop CPU	int4: 4.863 tokens/sec	fp32: 1.699 tokens/sec	2.8x
Intel i7 processor	int4: 3.474 tokens/sec fp32: 1.800 tokens/sec	fp32: 0.702 tokens/sec	4.85x

Files

27 files · 8.37 GBWeights 287 KB (smallest of 2 ONNX variants). Sizes and sha256 from the signed manifest.

Show all 27 files with their sha256

File	Size	sha256
.gitattributes	1.61 KB	6d37258060e6eabdd54ea002644865fdaaa41030cb2a01fb037333493b169d10
LICENSE	1.08 KB	fa8235e5b48faca34e3ca98cf4f694ef08bd216d28b58071a1f85b1d50cb814d
README.md	4.66 KB	a7b2d46a8e5bd4c98a06a20dd4ffe9a031294ff9ff366a801189d942651342fe
config.json	2.50 KB	ac65d86061d3d0d704ee2511fd0eb8713ef19eb6eedba17c3080a4165d5b933b
configuration_phi3.py	10.9 KB	ec2044b77d0b8111640ac134daa3af1f40dc552a739f8959626e7e58ea3352df
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/added_tokens.json	249 B	d4f2aceb0f20b71dd1f4bcc7e052e4412946bf281840b8f83d39f259571af486
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/config.json	2.50 KB	ac65d86061d3d0d704ee2511fd0eb8713ef19eb6eedba17c3080a4165d5b933b
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/configuration_phi3.py	10.9 KB	ec2044b77d0b8111640ac134daa3af1f40dc552a739f8959626e7e58ea3352df
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/genai_config.json	1.52 KB	0fcfa1e663f2bc867f8dc62fae65dd0924f0a4d68b43d1234df742dd19171470
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/merges.txt	2.42 MB	856ce61180bb689282eed6b3a6838bb1f438399be23aefe9d20eb379791fb4ad
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/model.onnx	52.1 MB	701aa5d185b6a782bc27104a990dd5b634fa507840b7c42f7ee6f1fb812d0b83
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/model.onnx.data	4.86 GB	cb0267fa60befa1a4ade8c98b6d32a3d67f51abbd307c7f793f132e8d9092131
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/special_tokens_map.json	587 B	aff38493227d813e29fcf8406e8e90062f1f031aa47d589325e9c31d89ac7cc3
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/tokenizer.json	15.5 MB	382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/tokenizer_config.json	2.96 KB	c565326a315fbe62cda093a59d298828c8f3f823122661325f41f3ba577a7dec
cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/vocab.json	3.91 MB	6cb65a857824fa6615bb1782d95d882617a8bbce1da0317118586b36f39e98bd
gpu/gpu-int4-rtn-block-32/added_tokens.json	249 B	d4f2aceb0f20b71dd1f4bcc7e052e4412946bf281840b8f83d39f259571af486
gpu/gpu-int4-rtn-block-32/config.json	2.50 KB	ac65d86061d3d0d704ee2511fd0eb8713ef19eb6eedba17c3080a4165d5b933b
gpu/gpu-int4-rtn-block-32/configuration_phi3.py	10.9 KB	ec2044b77d0b8111640ac134daa3af1f40dc552a739f8959626e7e58ea3352df
gpu/gpu-int4-rtn-block-32/genai_config.json	1.57 KB	3cdbd2ff0b2cda7829b262cda331a72b99daaa758d55bb056bb4d098d22b1140
gpu/gpu-int4-rtn-block-32/merges.txt	2.42 MB	856ce61180bb689282eed6b3a6838bb1f438399be23aefe9d20eb379791fb4ad
gpu/gpu-int4-rtn-block-32/model.onnx	287 KB	f8ff300b85719f79f1c220c88575626c62cc772a228244a6b96031d1a8d25250
gpu/gpu-int4-rtn-block-32/model.onnx.data	3.41 GB	b32f38f9211040b01c1483759cef7a6cce28df9f83b6b8bb91a474575d559d0f
gpu/gpu-int4-rtn-block-32/special_tokens_map.json	587 B	aff38493227d813e29fcf8406e8e90062f1f031aa47d589325e9c31d89ac7cc3
gpu/gpu-int4-rtn-block-32/tokenizer.json	15.5 MB	382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
gpu/gpu-int4-rtn-block-32/tokenizer_config.json	2.96 KB	c565326a315fbe62cda093a59d298828c8f3f823122661325f41f3ba577a7dec
gpu/gpu-int4-rtn-block-32/vocab.json	3.91 MB	6cb65a857824fa6615bb1782d95d882617a8bbce1da0317118586b36f39e98bd

VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.

Upstream commit	Date	Contents
fc04c8f93d pinned now	2026-11-15 03:45 UTC	27 files · 8.37 GB

Use it

$ fw get microsoft/Phi-4-mini-instruct-onnx
# manifest sha256 494297e8ecfc… · 8.37 GB · 27 files
$ fw verify ./Phi-4-mini-instruct-onnx microsoft/Phi-4-mini-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-4-mini-instruct-onnx.json --dir ./Phi-4-mini-instruct-onnx
$ python3 fw.py verify ./Phi-4-mini-instruct-onnx ./microsoft__Phi-4-mini-instruct-onnx.json

hfThe Hugging Face CLI with one setting. Same repo id.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/Phi-4-mini-instruct-onnx --revision fc04c8f93df696602fd9f300a30d1bf2e3081347 --local-dir ./Phi-4-mini-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
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:

microsoft <huggingface.co/microsoft>Newsgroups: alt.freeweights.phiSubject: Phi-4-mini-instruct-onnxDate: 2026-11-15Commit: fc04c8fSigned: 2DC28CC7B26A283B ✓

Upstream: microsoft/Phi-4-mini-instruct-onnx
Pinned commit: fc04c8f93df696602fd9f300a30d1bf2e3081347
Manifest: Phi-4-mini-instruct-onnx.json · .minisig
Manifest sha256: 494297e8ecfcd9fdda107854675f1a714f3aaa5c2cf7553c4c8e0cddce130c4d
Signature: minisign, Ed25519, key 2DC28CC7B26A283B
Created: 2026-11-15 03:45 UTC
Generator: freeweights-manifest/0.1.0
Takedowns: None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/microsoft/Phi-4-mini-instruct-onnx.json
$ curl -O https://freeweights.allternit.com/manifests/microsoft/Phi-4-mini-instruct-onnx.json.minisig
$ minisign -Vm Phi-4-mini-instruct-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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$ HF_ENDPOINT=https://freeweights.allternit.com hf download microsoft/Phi-4-mini-instruct-onnx

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

Signed manifest

minisign, key 2DC28CC7B26A283B. Phi-4-mini-instruct-onnx.json. Provenance. Takedown history: none.

Source

huggingface.co/microsoft/Phi-4-mini-instruct-onnx

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