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.github/workflows/install.yml:40","Warn: downloadThenRun not pinned by hash: .github/workflows/install.yml:63","Warn: pipCommand not pinned by hash: .github/workflows/install.yml:84","Warn: pipCommand not pinned by hash: .github/workflows/macos.yml:31","Warn: pipCommand not pinned by hash: .github/workflows/macos.yml:55","Warn: pipCommand not pinned by hash: .github/workflows/mypy.yml:27","Warn: pipCommand not pinned by hash: .github/workflows/mypy.yml:30","Warn: pipCommand not pinned by hash: .github/workflows/nightly.yml:44","Warn: pipCommand not pinned by hash: .github/workflows/python-publish.yml:35","Warn: pipCommand not pinned by hash: .github/workflows/python-publish.yml:36","Warn: pipCommand not pinned by hash: .github/workflows/sdl.yml:27","Info:  82 out of  82 GitHub-owned GitHubAction dependencies pinned","Info:  11 out of  12 third-party GitHubAction dependencies pinned","Info:   0 out of  37 pipCommand dependencies pinned","Info:   0 out of   4 downloadThenRun dependencies 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Project is vulnerable to: PYSEC-2025-40 / GHSA-qq3j-4f4f-9583","Warn: Project is vulnerable to: PYSEC-2024-227 / GHSA-qxrp-vhvm-j765","Warn: Project is vulnerable to: PYSEC-2023-301 / GHSA-v68g-wm8c-6x7j","Warn: Project is vulnerable to: PYSEC-2024-228 / GHSA-wrfc-pvp9-mr9g","Warn: Project is vulnerable to: PYSEC-2018-34 / GHSA-2fc2-6r4j-p65h","Warn: Project is vulnerable to: PYSEC-2021-856 / GHSA-5545-2q6w-2gh6","Warn: Project is vulnerable to: PYSEC-2019-108 / GHSA-9fq2-x9r6-wfmf","Warn: Project is vulnerable to: PYSEC-2018-33 / GHSA-cw6w-4rcx-xphc","Warn: Project is vulnerable to: PYSEC-2021-857 / GHSA-f7c7-j99h-c22f","Warn: Project is vulnerable to: GHSA-fpfv-jqm9-f5jm","Warn: Project is vulnerable to: PYSEC-2017-1 / GHSA-frgw-fgh6-9g52","Warn: Project is vulnerable to: PYSEC-2020-107 / GHSA-jjw5-xxj6-pcv5","Warn: Project is vulnerable to: PYSEC-2024-110 / GHSA-jw8x-6495-233v","Warn: Project is vulnerable to: PYSEC-2020-108","Warn: Project is vulnerable to: PYSEC-2017-74","Warn: Project is vulnerable to: GHSA-23hm-7w47-xw72","Warn: Project is vulnerable to: GHSA-2475-53vw-vp25","Warn: Project is vulnerable to: GHSA-247x-2f9f-5wp7","Warn: Project is vulnerable to: GHSA-24x4-6qmh-88qg","Warn: Project is vulnerable to: PYSEC-2021-227 / GHSA-24x6-8c7m-hv3f","Warn: Project is vulnerable to: PYSEC-2021-223 / GHSA-26j7-6w8w-7922","Warn: Project is vulnerable to: GHSA-278g-rq84-9hmg","Warn: Project is vulnerable to: PYSEC-2021-266 / GHSA-27j5-4p9v-pp67","Warn: Project is vulnerable to: PYSEC-2021-313 / GHSA-27qf-jwm8-g7f3","Warn: Project is vulnerable to: GHSA-27rc-728f-x5w2","Warn: Project is vulnerable to: PYSEC-2021-160 / GHSA-2cpx-427x-q2c6","Warn: Project is vulnerable to: PYSEC-2021-173 / GHSA-2gfx-95x2-5v3x","Warn: Project is vulnerable to: PYSEC-2021-391 / GHSA-2p25-55c9-h58q","Warn: Project is vulnerable to: GHSA-2p9q-h29j-3f5v","Warn: Project is vulnerable to: GHSA-2r2f-g8mw-9gvr","Warn: Project is vulnerable to: GHSA-2r8p-fg3c-wcj4","Warn: Project is vulnerable to: GHSA-2vv3-56qg-g2cf","Warn: Project is vulnerable to: GHSA-2wmv-37vq-52g5","Warn: Project is vulnerable to: PYSEC-2021-249 / GHSA-2xgj-xhgf-ggjv","Warn: Project is vulnerable to: GHSA-34f9-hjfq-rr8j","Warn: Project is vulnerable to: GHSA-368v-7v32-52fx","Warn: Project is vulnerable to: PYSEC-2021-199 / GHSA-36vm-xw34-x4pj","Warn: Project is vulnerable to: PYSEC-2021-418 / GHSA-374m-jm66-3vj8","Warn: Project is vulnerable to: GHSA-37jf-mjv6-xfqw","Warn: Project is vulnerable to: PYSEC-2021-170 / GHSA-393f-2jr3-cp69","Warn: Project is vulnerable to: GHSA-397c-5g2j-qxpv","Warn: Project is vulnerable to: PYSEC-2021-408 / GHSA-3ff2-r28g-w7h9","Warn: Project is vulnerable to: PYSEC-2021-206 / GHSA-3h8m-483j-7xxm","Warn: Project is vulnerable to: GHSA-3hxh-8cp2-g4hg","Warn: Project is vulnerable to: GHSA-3mw4-6rj6-74g5","Warn: Project is vulnerable to: PYSEC-2021-231 / GHSA-3qgw-p4fm-x7gf","Warn: Project is vulnerable to: PYSEC-2021-168 / GHSA-3qxp-qjq7-w4hf","Warn: Project is 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GHSA-4hvv-7x94-7vq8","Warn: Project is vulnerable to: GHSA-4j82-5ccr-4r8v","Warn: Project is vulnerable to: PYSEC-2021-205 / GHSA-4p4p-www8-8fv9","Warn: Project is vulnerable to: GHSA-4pc4-m9mj-v2r9","Warn: Project is vulnerable to: GHSA-4v5p-v5h9-6xjx","Warn: Project is vulnerable to: PYSEC-2021-163 / GHSA-4vf2-4xcg-65cx","Warn: Project is vulnerable to: PYSEC-2021-233 / GHSA-4vrf-ff7v-hpgr","Warn: Project is vulnerable to: GHSA-4w68-4x85-mjj9","Warn: Project is vulnerable to: PYSEC-2021-278 / GHSA-4xfp-4pfp-89wg","Warn: Project is vulnerable to: PYSEC-2021-207 / GHSA-545v-42p7-98fq","Warn: Project is vulnerable to: GHSA-54ch-gjq5-4976","Warn: Project is vulnerable to: GHSA-54pp-c6pp-7fpx","Warn: Project is vulnerable to: GHSA-558h-mq8x-7q9g","Warn: Project is vulnerable to: PYSEC-2021-400 / GHSA-57wx-m983-2f88","Warn: Project is vulnerable to: GHSA-5889-7v45-q28m","Warn: Project is vulnerable to: PYSEC-2021-196 / GHSA-59q2-x2qc-4c97","Warn: Project is vulnerable to: PYSEC-2021-409 / GHSA-5crj-c72x-m7gq","Warn: Project is vulnerable to: GHSA-5f2r-qp73-37mr","Warn: Project is vulnerable to: PYSEC-2021-209 / GHSA-5gqf-456p-4836","Warn: Project is vulnerable to: GHSA-5hj3-vjjf-f5m7","Warn: Project is vulnerable to: PYSEC-2021-392 / GHSA-5hx2-qx8j-qjqm","Warn: Project is vulnerable to: GHSA-5qw5-89mw-wcg2","Warn: Project is vulnerable to: GHSA-5v77-j66x-4c4g","Warn: Project is vulnerable to: GHSA-5w96-866f-6rm8","Warn: Project is vulnerable to: GHSA-5wpj-c6f7-24x8","Warn: Project is vulnerable to: GHSA-5xwc-mrhx-5g3m","Warn: Project is vulnerable to: GHSA-627q-g293-49q7","Warn: Project is vulnerable to: PYSEC-2021-155 / GHSA-62gx-355r-9fhg","Warn: Project is vulnerable to: PYSEC-2020-118 / GHSA-63xm-rx5p-xvqr","Warn: Project is vulnerable to: GHSA-6445-fm66-fvq2","Warn: Project is vulnerable to: GHSA-647v-r7qq-24fh","Warn: Project is vulnerable to: GHSA-64jg-wjww-7c5w","Warn: Project is vulnerable to: GHSA-66vq-54fq-6jvv","Warn: Project is vulnerable to: GHSA-67pf-62xr-q35m","Warn: Project is vulnerable to: GHSA-689c-r7h2-fv9v","Warn: Project is vulnerable to: GHSA-68v3-g9cm-rmm6","Warn: Project is vulnerable to: PYSEC-2021-165 / GHSA-6f84-42vf-ppwp","Warn: Project is vulnerable to: PYSEC-2021-215 / GHSA-6f89-8j54-29xf","Warn: Project is vulnerable to: GHSA-6gmv-pjp9-p8w8","Warn: Project is vulnerable to: PYSEC-2021-271 / GHSA-6gv8-p3vj-pxvr","Warn: Project is vulnerable to: GHSA-6hg6-5c2q-7rcr","Warn: Project is vulnerable to: PYSEC-2021-401 / GHSA-6hpv-v2rx-c5g6","Warn: Project is vulnerable to: PYSEC-2021-171 / GHSA-6j9c-grc6-5m6g","Warn: Project is vulnerable to: GHSA-6p5r-g9mq-ggh2","Warn: Project is vulnerable to: PYSEC-2021-212 / GHSA-6qgm-fv6v-rfpv","Warn: Project is vulnerable to: GHSA-6wfh-89q8-44jq","Warn: Project is vulnerable to: GHSA-6x99-gv2v-q76v","Warn: Project is vulnerable to: GHSA-75c9-jrh4-79mc","Warn: Project is vulnerable to: PYSEC-2021-201 / GHSA-75f6-78jr-4656","Warn: Project is vulnerable to: GHSA-762h-vpvw-3rcx","Warn: Project is vulnerable to: PYSEC-2021-156 / GHSA-772j-h9xw-ffp5","Warn: Project is vulnerable to: PYSEC-2021-154 / GHSA-772p-x54p-hjrv","Warn: Project is vulnerable to: GHSA-77gp-3h4r-6428","Warn: Project is vulnerable to: PYSEC-2021-397 / GHSA-786j-5qwq-r36x","Warn: Project is vulnerable to: PYSEC-2021-216 / GHSA-79fv-9865-4qcv","Warn: Project is vulnerable to: GHSA-79h2-q768-fpxr","Warn: Project is vulnerable to: PYSEC-2021-213 / GHSA-7cqx-92hp-x6wh","Warn: Project is vulnerable to: GHSA-7fvx-3jfc-2cpc","Warn: Project is vulnerable to: PYSEC-2021-296 / GHSA-7ghq-fvr3-pj2x","Warn: Project is vulnerable to: GHSA-7j3m-8g3c-9qqq","Warn: Project is vulnerable to: GHSA-7jvm-xxmr-v5cw","Warn: Project is vulnerable to: PYSEC-2021-396 / GHSA-7pxj-m4jf-r6h2","Warn: Project is vulnerable to: PYSEC-2021-417 / GHSA-7r94-xv9v-63jw","Warn: Project is vulnerable to: PYSEC-2021-399 / GHSA-7v94-64hj-m82h","Warn: Project is vulnerable to: GHSA-7x4v-9gxg-9hwj","Warn: Project is vulnerable to: PYSEC-2021-303 / GHSA-7xwj-5r4v-429p","Warn: Project is vulnerable to: GHSA-828c-5j5q-vrjq","Warn: Project is vulnerable to: PYSEC-2021-211 / GHSA-828x-qc2p-wprq","Warn: Project is vulnerable to: GHSA-844w-j86r-4x2j","Warn: Project is vulnerable to: GHSA-84jm-4cf3-9jfm","Warn: Project is vulnerable to: PYSEC-2021-153 / GHSA-84mw-34w6-2q43","Warn: Project is vulnerable to: GHSA-87v6-crgm-2gfj","Warn: Project is vulnerable to: PYSEC-2021-174 / GHSA-8c89-2vwr-chcq","Warn: Project is vulnerable to: GHSA-8cxv-76p7-jxwr","Warn: Project is vulnerable to: GHSA-8fvv-46hw-vpg3","Warn: Project is vulnerable to: PYSEC-2021-197 / GHSA-8gv3-57p6-g35r","Warn: Project is vulnerable to: GHSA-8jj7-5vxc-pg2q","Warn: Project is vulnerable to: PYSEC-2021-251 / GHSA-8pmx-p244-g88h","Warn: Project is vulnerable to: GHSA-8r7c-3cm2-3h8f","Warn: Project is vulnerable to: GHSA-8rcj-c8pj-v3m3","Warn: Project is vulnerable to: PYSEC-2021-241 / GHSA-8rm6-75mf-7r7r","Warn: Project is vulnerable to: GHSA-8w5g-3wcv-9g2j","Warn: Project is vulnerable to: GHSA-8wwm-6264-x792","Warn: Project is vulnerable to: GHSA-93vr-9q9m-pj8p","Warn: Project is vulnerable to: GHSA-94mm-g2mv-8p7r","Warn: Project is vulnerable to: PYSEC-2021-262 / GHSA-95xm-g58g-3p88","Warn: Project is vulnerable to: PYSEC-2021-292 / GHSA-9697-98pf-4rw7","Warn: Project is vulnerable to: PYSEC-2020-258 / GHSA-977j-xj7q-2jr9","Warn: Project is vulnerable to: GHSA-97p7-w86h-vcf9","Warn: Project is vulnerable to: PYSEC-2021-236 / GHSA-97wf-p777-86jq","Warn: Project is vulnerable to: GHSA-98j8-c9q4-r38g","Warn: Project is vulnerable to: GHSA-98p5-x8x4-c9m5","Warn: Project is vulnerable to: GHSA-9942-r22v-78cp","Warn: Project is vulnerable to: GHSA-9c78-vcq7-7vxq","Warn: Project is vulnerable to: PYSEC-2021-238 / GHSA-9c84-4hx6-xmm4","Warn: Project is vulnerable to: PYSEC-2021-297 / GHSA-9c8h-2mv3-49ww","Warn: Project is vulnerable to: GHSA-9c8h-vvrj-w2p8","Warn: Project is vulnerable to: GHSA-9cr2-8pwr-fhfq","Warn: Project is vulnerable to: PYSEC-2021-410 / GHSA-9crf-c6qr-r273","Warn: Project is vulnerable to: GHSA-9fpg-838v-wpv7","Warn: Project is vulnerable to: GHSA-9gwq-6cwj-47h3","Warn: Project is vulnerable to: GHSA-9j4v-pp28-mxv7","Warn: Project is vulnerable to: PYSEC-2020-117 / GHSA-9mqp-7v2h-2382","Warn: Project is vulnerable to: GHSA-9p77-mmrw-69c7","Warn: Project is vulnerable to: GHSA-9px9-73fg-3fqp","Warn: Project is vulnerable to: GHSA-9v8w-xmr4-wgxp","Warn: Project is vulnerable to: PYSEC-2021-210 / GHSA-9vpm-rcf4-9wqw","Warn: Project is vulnerable to: GHSA-9vqj-64pv-w55c","Warn: Project is vulnerable to: PYSEC-2021-267 / GHSA-9w2p-5mgw-p94c","Warn: Project is vulnerable to: PYSEC-2021-220 / GHSA-9xh4-23q4-v6wr","Warn: Project is vulnerable to: PYSEC-2021-219 / GHSA-c45w-2wxr-pp53","Warn: Project is vulnerable to: GHSA-c545-c4f9-rf6v","Warn: Project is vulnerable to: GHSA-c582-c96p-r5cq","Warn: Project is vulnerable to: GHSA-c5x2-p679-95wc","Warn: Project is vulnerable to: GHSA-c6fh-56w7-fvjw","Warn: Project is vulnerable to: GHSA-c94w-c95p-phf8","Warn: Project is vulnerable to: PYSEC-2021-159 / GHSA-c968-pq7h-7fxv","Warn: Project is vulnerable to: PYSEC-2020-140 / GHSA-c9f3-9wfr-wgh7","Warn: Project is vulnerable to: PYSEC-2021-259 / GHSA-c9qf-r67m-p7cg","Warn: Project is vulnerable to: PYSEC-2021-302 / GHSA-cfpj-3q4c-jhvr","Warn: Project is vulnerable to: PYSEC-2021-230 / GHSA-cfx7-2xpc-8w4h","Warn: Project is vulnerable to: GHSA-cg88-rpvp-cjv5","Warn: Project is vulnerable to: PYSEC-2021-257 / GHSA-cgfm-62j4-v4rf","Warn: Project is vulnerable to: PYSEC-2021-264 / GHSA-ch4f-829c-v5pw","Warn: Project is vulnerable to: PYSEC-2021-246 / GHSA-cjc7-49v2-jp64","Warn: Project is vulnerable to: GHSA-cm5x-837x-jf3c","Warn: Project is vulnerable to: PYSEC-2021-414 / GHSA-cpf4-wx82-gxp6","Warn: Project is vulnerable to: PYSEC-2021-842 / GHSA-cq76-mxrc-vchh","Warn: Project is vulnerable to: PYSEC-2021-413 / GHSA-cqv6-3phm-hcwx","Warn: Project is vulnerable to: GHSA-cqvq-fvhr-v6hc","Warn: Project is vulnerable to: PYSEC-2021-240 / GHSA-crch-j389-5f84","Warn: Project is vulnerable to: GHSA-cv2p-32v3-vhwq","Warn: Project is vulnerable to: PYSEC-2020-134 / GHSA-cvpc-8phh-8f45","Warn: Project is vulnerable to: GHSA-cwpm-f78v-7m5c","Warn: Project is vulnerable to: PYSEC-2021-228 / GHSA-cwv3-863g-39vx","Warn: Project is vulnerable to: GHSA-f2vv-v9cg-qhh7","Warn: Project is vulnerable to: GHSA-f2w8-jw48-fr7j","Warn: Project is vulnerable to: GHSA-f49c-87jh-g47q","Warn: Project is vulnerable to: GHSA-f4rr-5m7v-wxcw","Warn: Project is vulnerable to: GHSA-f4w6-h4f5-wx45","Warn: Project is vulnerable to: PYSEC-2021-415 / GHSA-f54p-f6jp-4rhr","Warn: Project is vulnerable to: PYSEC-2021-284 / GHSA-f5cx-5wr3-5qrc","Warn: Project is vulnerable to: GHSA-f637-vh3r-vfh2","Warn: Project is vulnerable to: PYSEC-2021-187 / GHSA-f78g-q7r4-9wcv","Warn: Project is vulnerable to: GHSA-f7r5-q7cx-h668","Warn: Project is vulnerable to: GHSA-f8h4-7rgh-q2gm","Warn: Project is vulnerable to: PYSEC-2021-265 / GHSA-fcwc-p4fc-c5cc","Warn: Project is vulnerable to: GHSA-ffjm-4qwc-7cmf","Warn: Project is vulnerable to: GHSA-fhfc-2q7x-929f","Warn: Project is vulnerable to: GHSA-fpcp-9h7m-ffpx","Warn: Project is vulnerable to: PYSEC-2021-180 / GHSA-fphq-gw9m-ghrv","Warn: Project is vulnerable to: GHSA-fq6p-6334-8gr4","Warn: Project is vulnerable to: GHSA-fq86-3f29-px2c","Warn: Project is vulnerable to: GHSA-fqm2-gh8w-gr68","Warn: Project is vulnerable to: GHSA-fqxc-pvf8-2w9v","Warn: Project is vulnerable to: PYSEC-2021-404 / GHSA-fr77-rrx3-cp7g","Warn: Project is vulnerable to: GHSA-frqp-wp83-qggv","Warn: Project is vulnerable to: GHSA-fv25-wrff-wf86","Warn: Project is vulnerable to: GHSA-fv43-93gv-vm8f","Warn: Project is vulnerable to: GHSA-fx5c-h9f6-rv7c","Warn: Project is vulnerable to: GHSA-fxgc-95xx-grvq","Warn: Project is vulnerable to: PYSEC-2021-193 / GHSA-fxqh-cfjm-fp93","Warn: Project is vulnerable to: PYSEC-2021-285 / GHSA-g25h-jr74-qp5j","Warn: Project is vulnerable to: GHSA-g35r-369w-3fqp","Warn: Project is vulnerable to: GHSA-g468-qj8g-vcjc","Warn: Project is vulnerable to: PYSEC-2021-176 / GHSA-g4h2-gqm3-c9wq","Warn: Project is vulnerable to: PYSEC-2020-128 / GHSA-g7p5-5759-qv46","Warn: Project is vulnerable to: PYSEC-2021-301 / GHSA-g8wg-cjwc-xhhp","Warn: Project is vulnerable to: GHSA-g9fm-r5mm-rf9f","Warn: Project is vulnerable to: GHSA-g9h5-vr8m-x2h4","Warn: Project is vulnerable to: GHSA-gcvh-66ff-4mwm","Warn: Project is vulnerable to: GHSA-gf2j-f278-xh4v","Warn: Project is vulnerable to: PYSEC-2021-283 / GHSA-gf88-j2mg-cc82","Warn: Project is vulnerable to: GHSA-gf97-q72m-7579","Warn: Project is vulnerable to: PYSEC-2021-261 / GHSA-gh6x-4whr-2qv4","Warn: Project is vulnerable to: PYSEC-2021-393 / GHSA-gh8h-7j2j-qv4f","Warn: Project is vulnerable to: GHSA-gjh7-xx4r-x345","Warn: Project is vulnerable to: GHSA-gjqc-q9g6-q2j3","Warn: Project is vulnerable to: GHSA-gq2j-cr96-gvqx","Warn: Project is vulnerable to: PYSEC-2021-244 / GHSA-gv26-jpj9-c8gq","Warn: Project is vulnerable to: PYSEC-2021-198 / GHSA-gvm4-h8j3-rjrq","Warn: Project is vulnerable to: GHSA-gw97-ff7c-9v96","Warn: Project is vulnerable to: GHSA-h246-cgh4-7475","Warn: Project is vulnerable to: GHSA-h2wq-prv9-2f56","Warn: Project is vulnerable to: GHSA-h48f-q7rw-hvr7","Warn: Project is vulnerable to: PYSEC-2021-243 / GHSA-h4pc-gx2w-f2xv","Warn: Project is vulnerable to: GHSA-h5g4-ppwx-48q2","Warn: Project is vulnerable to: GHSA-h5vq-gw2c-pq47","Warn: Project is vulnerable to: PYSEC-2021-405 / GHSA-h67m-xg8f-fxcf","Warn: Project is vulnerable to: PYSEC-2020-125 / GHSA-h6fg-mjxg-hqq4","Warn: Project is vulnerable to: GHSA-h6gw-r52c-724r","Warn: Project is vulnerable to: GHSA-h6jh-7gv5-28vg","Warn: Project is vulnerable to: GHSA-h6q3-vv32-2cq5","Warn: Project is vulnerable to: GHSA-h7ff-cfc9-wmmh","Warn: Project is vulnerable to: PYSEC-2020-253 / GHSA-h98h-8mxr-m8gx","Warn: Project is vulnerable to: PYSEC-2021-190 / GHSA-h9px-9vqg-222h","Warn: Project is vulnerable to: PYSEC-2021-152 / GHSA-hc6c-75p4-hmq4","Warn: Project is vulnerable to: PYSEC-2020-255 / GHSA-hhvc-g5hv-48c6","Warn: Project is vulnerable to: PYSEC-2021-182 / GHSA-hmg3-c7xj-6qwm","Warn: Project is vulnerable to: GHSA-hp4c-x6r7-6555","Warn: Project is vulnerable to: GHSA-hpv4-7p9c-mvfr","Warn: Project is vulnerable to: GHSA-hq7g-wwwp-q46h","Warn: Project is vulnerable to: GHSA-hrg5-737c-2p56","Warn: Project is vulnerable to: PYSEC-2021-260 / GHSA-hwr7-8gxx-fj5p","Warn: Project is vulnerable to: GHSA-hx9q-2mx4-m4pg","Warn: Project is vulnerable to: GHSA-j3mj-fhpq-qqjj","Warn: Project is vulnerable to: GHSA-j43h-pgmg-5hjq","Warn: Project is vulnerable to: PYSEC-2021-169 / GHSA-j47f-4232-hvv8","Warn: Project is vulnerable to: GHSA-j5w9-hmfh-4cr6","Warn: Project is vulnerable to: PYSEC-2021-224 / GHSA-j7rm-8ww4-xx2g","Warn: Project is vulnerable to: PYSEC-2021-394 / GHSA-j86v-p27c-73fm","Warn: Project is vulnerable to: PYSEC-2021-419 / GHSA-j8c8-67vp-6mx7","Warn: Project is vulnerable to: PYSEC-2021-175 / GHSA-j8qc-5fqr-52fp","Warn: Project is vulnerable to: PYSEC-2021-237 / GHSA-j8qh-3xrq-c825","Warn: Project is vulnerable to: PYSEC-2021-242 / GHSA-jf7h-7m85-w2v2","Warn: Project is vulnerable to: PYSEC-2021-166 / GHSA-jfp7-4j67-8r3q","Warn: Project is vulnerable to: PYSEC-2021-189 / GHSA-jhq9-wm9m-cf89","Warn: Project is vulnerable to: GHSA-jjm6-4vf7-cjh4","Warn: Project is vulnerable to: PYSEC-2021-229 / GHSA-jjr8-m8g8-p6wv","Warn: Project is vulnerable to: GHSA-jq6x-99hj-q636","Warn: Project is vulnerable to: GHSA-jqm7-m5q7-3hm5","Warn: Project is vulnerable to: GHSA-jvhc-5hhr-w3v5","Warn: Project is vulnerable to: GHSA-jwf9-w5xm-f437","Warn: Project is vulnerable to: PYSEC-2021-402 / GHSA-m342-ff57-4jcc","Warn: Project is vulnerable to: PYSEC-2021-183 / GHSA-m34j-p8rj-wjxq","Warn: Project is vulnerable to: PYSEC-2021-172 / GHSA-m3f9-w3p3-p669","Warn: Project is vulnerable to: GHSA-m4hf-j54p-p353","Warn: Project is vulnerable to: PYSEC-2021-389 / GHSA-m539-j985-hcr8","Warn: Project is vulnerable to: PYSEC-2020-256 / GHSA-m648-33qf-v3gp","Warn: Project is vulnerable to: GHSA-m6cv-4fmf-66xf","Warn: Project is vulnerable to: GHSA-m6vp-8q9j-whx4","Warn: Project is vulnerable to: PYSEC-2021-274 / GHSA-m7fm-4jfh-jrg6","Warn: Project is vulnerable to: GHSA-mg66-qvc5-rm93","Warn: Project is vulnerable to: GHSA-mgmh-g2v6-mqw5","Warn: Project is vulnerable to: GHSA-mh3m-62v7-68xg","Warn: Project is vulnerable to: PYSEC-2021-254 / GHSA-mmq6-q8r3-48fm","Warn: Project is vulnerable to: PYSEC-2021-247 / GHSA-mq5c-prh3-3f3h","Warn: Project is vulnerable to: PYSEC-2021-195 / GHSA-mqh2-9wrp-vx84","Warn: Project is vulnerable to: GHSA-mv77-9g28-cwg3","Warn: Project is vulnerable to: PYSEC-2021-222 / GHSA-mv78-g7wq-mhp4","Warn: Project is vulnerable to: GHSA-mv8m-8x97-937q","Warn: Project is vulnerable to: GHSA-mw6j-hh29-h379","Warn: Project is vulnerable to: PYSEC-2020-131 / GHSA-mxjj-953w-2c2v","Warn: Project is vulnerable to: GHSA-p2xf-8hgm-hpw5","Warn: Project is vulnerable to: PYSEC-2021-185 / GHSA-p45v-v4pw-77jr","Warn: Project is vulnerable to: GHSA-p7hr-f446-x6qf","Warn: Project is vulnerable to: GHSA-p9rc-rmr5-529j","Warn: Project is vulnerable to: GHSA-pf36-r9c6-h97j","Warn: Project is vulnerable to: GHSA-pfjj-m3jj-9jc9","Warn: Project is vulnerable to: PYSEC-2021-843 / GHSA-pgcq-h79j-2f69","Warn: Project is vulnerable to: PYSEC-2021-200 / GHSA-ph87-fvjr-v33w","Warn: Project is vulnerable to: PYSEC-2021-235 / GHSA-pmpr-55fj-r229","Warn: Project is vulnerable to: GHSA-pqhm-4wvf-2jg8","Warn: Project is vulnerable to: GHSA-pqrv-8r2f-7278","Warn: Project is vulnerable to: PYSEC-2021-390 / GHSA-prcg-wp5q-rv7p","Warn: Project is vulnerable to: PYSEC-2021-203 / GHSA-pvrc-hg3f-58r6","Warn: Project is vulnerable to: GHSA-pxrw-j2fv-hx3h","Warn: Project is vulnerable to: PYSEC-2020-257 / GHSA-q263-fvxm-m5mw","Warn: Project is vulnerable to: GHSA-q2c3-jpmc-gfjx","Warn: Project is vulnerable to: PYSEC-2021-281 / GHSA-q3g3-h9r4-prrc","Warn: Project is vulnerable to: PYSEC-2020-130 / GHSA-q4qf-3fc6-8x34","Warn: Project is vulnerable to: GHSA-q5jv-m6qw-5g37","Warn: Project is vulnerable to: GHSA-q7f7-544h-67h9","Warn: Project is vulnerable to: GHSA-q85f-69q7-55h2","Warn: Project is vulnerable to: PYSEC-2020-127 / GHSA-q8gv-q7wr-9jf8","Warn: Project is vulnerable to: GHSA-qfpc-5pjr-mh26","Warn: Project is vulnerable to: PYSEC-2020-132 / GHSA-qh32-6jjc-qprm","Warn: Project is vulnerable to: GHSA-qhw4-wwr7-gjc5","Warn: Project is vulnerable to: PYSEC-2020-254 / GHSA-qhxx-j73r-qpm2","Warn: Project is vulnerable to: GHSA-qj5r-f9mv-rffh","Warn: Project is vulnerable to: GHSA-qjj8-32p7-h289","Warn: Project is vulnerable to: GHSA-qjqc-vqcf-5qvj","Warn: Project is vulnerable to: PYSEC-2021-293 / GHSA-qr82-2c78-4m8h","Warn: Project is vulnerable to: PYSEC-2021-252 / GHSA-qw5h-7f53-xrp6","Warn: Project is vulnerable to: GHSA-qx3f-p745-w4hr","Warn: Project is vulnerable to: GHSA-qxpx-j395-pw36","Warn: Project is vulnerable to: GHSA-r26c-679w-mrjm","Warn: Project is vulnerable to: PYSEC-2021-192 / GHSA-r35g-4525-29fq","Warn: Project is vulnerable to: GHSA-r4c4-5fpq-56wg","Warn: Project is vulnerable to: PYSEC-2021-161 / GHSA-r4pj-74mg-8868","Warn: Project is vulnerable to: PYSEC-2021-300 / GHSA-r6jx-9g48-2r5r","Warn: Project is vulnerable to: PYSEC-2021-202 / GHSA-r6pg-pjwc-j585","Warn: Project is vulnerable to: GHSA-rc9w-5c64-9vqq","Warn: Project is vulnerable to: GHSA-rcf8-g8jv-vg6p","Warn: Project is vulnerable to: PYSEC-2021-239 / GHSA-rf3h-xgv5-2q39","Warn: Project is vulnerable to: PYSEC-2021-416 / GHSA-rg3m-hqc5-344v","Warn: Project is vulnerable to: PYSEC-2021-245 / GHSA-rgvq-pcvf-hx75","Warn: Project is vulnerable to: GHSA-rh87-q4vg-m45j","Warn: Project is vulnerable to: GHSA-rhrq-64mq-hf9h","Warn: Project is vulnerable to: GHSA-rjx6-v474-2ch9","Warn: Project is vulnerable to: GHSA-rmg2-f698-wq35","Warn: Project is vulnerable to: PYSEC-2020-138 / GHSA-rrfp-j2mp-hq9c","Warn: Project is vulnerable to: GHSA-rrx2-r989-2c43","Warn: Project is vulnerable to: GHSA-rww7-2gpw-fv6j","Warn: Project is vulnerable to: GHSA-v3f7-j968-4h5f","Warn: Project is vulnerable to: PYSEC-2021-234 / GHSA-v52p-hfjf-wg88","Warn: Project is vulnerable to: GHSA-v5xg-3q2c-c2r4","Warn: Project is vulnerable to: GHSA-v6h3-348g-6h5x","Warn: Project is vulnerable to: PYSEC-2021-214 / GHSA-v6r6-84gr-92rm","Warn: Project is vulnerable to: PYSEC-2021-298 / GHSA-v768-w7m9-2vmm","Warn: Project is vulnerable to: GHSA-v7vw-577f-vp8x","Warn: Project is vulnerable to: PYSEC-2021-287 / GHSA-v82p-hv3v-p6qp","Warn: Project is vulnerable to: PYSEC-2021-310 / GHSA-vcjj-9vg7-vf68","Warn: Project is vulnerable to: PYSEC-2021-232 / GHSA-vf94-36g5-69v8","Warn: Project is vulnerable to: PYSEC-2021-225 / GHSA-vfr4-x8j2-3rf9","Warn: Project is vulnerable to: GHSA-vgvh-2pf4-jr2x","Warn: Project is vulnerable to: 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align=\"center\"\u003e\n\n# Neural Network Compression Framework (NNCF)\n\n[Key Features](#key-features) •\n[Installation](#installation-guide) •\n[Documentation](#documentation) •\n[Usage](#usage) •\n[Tutorials and Samples](#demos-tutorials-and-samples) •\n[Third-party integration](#third-party-repository-integration) •\n[Model Zoo](./docs/ModelZoo.md)\n\n[![GitHub Release](https://img.shields.io/github/v/release/openvinotoolkit/nncf?color=green)](https://github.com/openvinotoolkit/nncf/releases)\n[![Website](https://img.shields.io/website?up_color=blue\u0026up_message=docs\u0026url=https%3A%2F%2Fdocs.openvino.ai%2Fnncf)](https://docs.openvino.ai/nncf)\n[![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)\n[![PyPI Downloads](https://static.pepy.tech/badge/nncf)](https://pypi.org/project/nncf/)\n\n![Python](https://img.shields.io/badge/python-3.10+-blue)\n![Backends](https://img.shields.io/badge/backends-openvino_|_pytorch_|_onnx_-orange)\n![OS](https://img.shields.io/badge/OS-Linux_|_Windows_|_MacOS-blue)\n\n\u003c/div\u003e\n\nNeural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for\noptimizing inference of neural networks in [OpenVINO\u0026trade;](https://docs.openvino.ai) with a minimal accuracy drop.\n\nNNCF is designed to work with models from [PyTorch](https://pytorch.org/),\n[TorchFX](https://pytorch.org/docs/stable/fx.html),\n[ONNX](https://onnx.ai/) and [OpenVINO\u0026trade;](https://docs.openvino.ai).\n\nNNCF provides [samples](#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different\nuse cases and models. See compression results achievable with the NNCF-powered samples on the [NNCF Model Zoo page](./docs/ModelZoo.md).\n\nThe framework is organized as a Python\\* package that can be built and used in a standalone mode. The framework\narchitecture is unified to make it easy to add different compression algorithms for both PyTorch deep\nlearning frameworks.\n\n\u003ca id=\"key-features\"\u003e\u003c/a\u003e\n\n## Key Features\n\n### Post-Training Compression Algorithms\n\n| Compression algorithm                                                                                    | OpenVINO      | PyTorch      | TorchFX       | ONNX          |\n| :------------------------------------------------------------------------------------------------------- | :-----------: | :----------: | :-----------: | :-----------: |\n| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported     | Supported    | Experimental  | Supported     |\n| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md)               | Supported     | Supported    | Experimental  | Supported     |\n| [Activation Sparsity](./src/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md)          | Not supported | Experimental | Not supported | Not supported |\n\n### Training-Time Compression Algorithms\n\n| Compression algorithm                                                                                                                         | PyTorch   |\n| :-------------------------------------------------------------------------------------------------------------------------------------------- | :-------: |\n| [Quantization Aware Training](./docs/usage/training_time_compression/quantization_aware_training/Usage.md)                                    | Supported |\n| [Weight-Only Quantization Aware Training with LoRA and NLS](./docs/usage/training_time_compression/quantization_aware_training_lora/Usage.md) | Supported |\n| [Pruning](./docs/usage/training_time_compression/pruning/Usage.md)                                                                            | Supported |\n\n- Automatic, configurable model graph transformation to obtain the compressed model.\n- Common interface for compression methods.\n- GPU-accelerated layers for faster compressed model fine-tuning.\n- Distributed training support.\n- Git patch for prominent third-party repository ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines.\n- Exporting PyTorch compressed models to ONNX\\* checkpoints compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO\u0026trade; toolkit](https://docs.openvino.ai).\n\n\u003ca id=\"documentation\"\u003e\u003c/a\u003e\n\n## Documentation\n\nThis documentation covers detailed information about NNCF algorithms and functions needed for the contribution to NNCF.\n\nThe latest user documentation for NNCF is available [here](https://docs.openvino.ai/nncf).\n\nNNCF API documentation can be found [here](https://openvinotoolkit.github.io/nncf/autoapi/nncf/).\n\n\u003ca id=\"usage\"\u003e\u003c/a\u003e\n\n## Usage\n\n### Post-Training Quantization\n\nThe NNCF PTQ is the simplest way to apply 8-bit quantization. To run the algorithm you only need your model and a small (~300 samples) calibration dataset.\n\n[OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run PTQ with, while PyTorch and ONNX are also supported.\n\n\u003cdetails open\u003e\u003csummary\u003e\u003cb\u003eOpenVINO\u003c/b\u003e\u003c/summary\u003e\n\n```python\nimport nncf\nimport openvino as ov\nimport torch\nfrom torchvision import datasets, transforms\n\n# Instantiate your uncompressed model\nmodel = ov.Core().read_model(\"/model_path\")\n\n# Provide validation part of the dataset to collect statistics needed for the compression algorithm\nval_dataset = datasets.ImageFolder(\"/path\", transform=transforms.Compose([transforms.ToTensor()]))\ndataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)\n\n# Step 1: Initialize transformation function\ndef transform_fn(data_item):\n    images, _ = data_item\n    return images\n\n# Step 2: Initialize NNCF Dataset\ncalibration_dataset = nncf.Dataset(dataset_loader, transform_fn)\n# Step 3: Run the quantization pipeline\nquantized_model = nncf.quantize(model, calibration_dataset)\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003ePyTorch\u003c/b\u003e\u003c/summary\u003e\n\n```python\nimport nncf\nimport torch\nfrom torchvision import datasets, models\n\n# Instantiate your uncompressed model\nmodel = models.mobilenet_v2()\n\n# Provide validation part of the dataset to collect statistics needed for the compression algorithm\nval_dataset = datasets.ImageFolder(\"/path\", transform=transforms.Compose([transforms.ToTensor()]))\ndataset_loader = torch.utils.data.DataLoader(val_dataset)\n\n# Step 1: Initialize the transformation function\ndef transform_fn(data_item):\n    images, _ = data_item\n    return images\n\n# Step 2: Initialize NNCF Dataset\ncalibration_dataset = nncf.Dataset(dataset_loader, transform_fn)\n# Step 3: Run the quantization pipeline\nquantized_model = nncf.quantize(model, calibration_dataset)\n\n```\n\n**NOTE** If the Post-Training Quantization algorithm does not meet quality requirements you can fine-tune the quantized pytorch model. You can find an example of the Quantization-Aware training pipeline for a pytorch model [here](examples/quantization_aware_training/torch/resnet18/README.md).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003eTorchFX\u003c/b\u003e\u003c/summary\u003e\n\n```python\nimport nncf\nimport torch.fx\nfrom torchvision import datasets, models\n\n# Instantiate your uncompressed model\nmodel = models.mobilenet_v2()\n\n# Provide validation part of the dataset to collect statistics needed for the compression algorithm\nval_dataset = datasets.ImageFolder(\"/path\", transform=transforms.Compose([transforms.ToTensor()]))\ndataset_loader = torch.utils.data.DataLoader(val_dataset)\n\n# Step 1: Initialize the transformation function\ndef transform_fn(data_item):\n    images, _ = data_item\n    return images\n\n# Step 2: Initialize NNCF Dataset\ncalibration_dataset = nncf.Dataset(dataset_loader, transform_fn)\n\n# Step 3: Export model to TorchFX\ninput_shape = (1, 3, 224, 224)\nfx_model = torch.export.export_for_training(model, args=(ex_input,)).module()\n# or\n# fx_model = torch.export.export(model, args=(ex_input,)).module()\n\n# Step 4: Run the quantization pipeline\nquantized_fx_model = nncf.quantize(fx_model, calibration_dataset)\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003eONNX\u003c/b\u003e\u003c/summary\u003e\n\n```python\nimport onnx\nimport nncf\nimport torch\nfrom torchvision import datasets\n\n# Instantiate your uncompressed model\nonnx_model = onnx.load_model(\"/model_path\")\n\n# Provide validation part of the dataset to collect statistics needed for the compression algorithm\nval_dataset = datasets.ImageFolder(\"/path\", transform=transforms.Compose([transforms.ToTensor()]))\ndataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)\n\n# Step 1: Initialize transformation function\ninput_name = onnx_model.graph.input[0].name\ndef transform_fn(data_item):\n    images, _ = data_item\n    return {input_name: images.numpy()}\n\n# Step 2: Initialize NNCF Dataset\ncalibration_dataset = nncf.Dataset(dataset_loader, transform_fn)\n# Step 3: Run the quantization pipeline\nquantized_model = nncf.quantize(onnx_model, calibration_dataset)\n```\n\n\u003c/details\u003e\n\n[//]: # (NNCF provides full  [samples]\u0026#40;#post-training-quantization-samples\u0026#41;, which demonstrate Post-Training Quantization usage for PyTorch, ONNX, and OpenVINO.)\n\n### Training-Time Quantization\n\nHere is an example of Accuracy Aware Quantization pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy.\n\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003ePyTorch\u003c/b\u003e\u003c/summary\u003e\n\n```python\nimport nncf\nimport torch\nfrom torchvision import datasets, models\n\n# Instantiate your uncompressed model\nmodel = models.mobilenet_v2()\n\n# Provide validation part of the dataset to collect statistics needed for the compression algorithm\nval_dataset = datasets.ImageFolder(\"/path\", transform=transforms.Compose([transforms.ToTensor()]))\ndataset_loader = torch.utils.data.DataLoader(val_dataset)\n\n# Step 1: Initialize the transformation function\ndef transform_fn(data_item):\n    images, _ = data_item\n    return images\n\n# Step 2: Initialize NNCF Dataset\ncalibration_dataset = nncf.Dataset(dataset_loader, transform_fn)\n# Step 3: Run the quantization pipeline\nquantized_model = nncf.quantize(model, calibration_dataset)\n\n# Now use compressed_model as a usual torch.nn.Module\n# to fine-tune compression parameters along with the model weights\n\n# Save quantization modules and the quantized model parameters\ncheckpoint = {\n    'state_dict': model.state_dict(),\n    'nncf_config': nncf.torch.get_config(model),\n    ... # the rest of the user-defined objects to save\n}\ntorch.save(checkpoint, path_to_checkpoint)\n\n# ...\n\n# Load quantization modules and the quantized model parameters\nresuming_checkpoint = torch.load(path_to_checkpoint)\nnncf_config = resuming_checkpoint['nncf_config']\nstate_dict = resuming_checkpoint['state_dict']\n\nquantized_model = nncf.torch.load_from_config(model, nncf_config)\nquantized_model.load_state_dict(state_dict)\n# ... the rest of the usual PyTorch-powered training pipeline\n```\n\n\u003c/details\u003e\n\n\u003ca id=\"demos-tutorials-and-samples\"\u003e\u003c/a\u003e\n\n## Demos, Tutorials and Samples\n\nFor a quicker start with NNCF-powered compression, try sample notebooks and scripts presented below.\n\n### Jupyter* Notebook Tutorials and Demos\n\nReady-to-run Jupyter* notebook tutorials and demos are available to explain and display NNCF compression algorithms for optimizing models for inference with the OpenVINO Toolkit:\n\n| Notebook Tutorial Name                                                                                                                                                                                                                                                                                                                                 |                                  Compression Algorithm                                  |  Backend   |               Domain                |\n|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------:|:----------:|:-----------------------------------:|\n| [BERT Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/language-quantize-bert)\u003cbr\u003e[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/language-quantize-bert/language-quantize-bert.ipynb) |                               Post-Training Quantization                                |  OpenVINO  |                 NLP                 |\n| [MONAI Segmentation Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/ct-segmentation-quantize)\u003cbr\u003e[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2Fct-segmentation-quantize%2Fct-scan-live-inference.ipynb)     |                               Post-Training Quantization                                |  OpenVINO  |            Segmentation             |\n| [PyTorch Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/pytorch-post-training-quantization-nncf)                                                                                                                                                                                                      |                               Post-Training Quantization                                |  PyTorch   |        Image Classification         |\n| [YOLOv11 Quantization with Accuracy Control](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov11-quantization-with-accuracy-control)                                                                                                                                                                                               |                    Post-Training Quantization with Accuracy Control                     |  OpenVINO  | Speech-to-Text,\u003cbr\u003eObject Detection |\n\nA list of notebooks demonstrating OpenVINO conversion and inference together with NNCF compression for models from various domains:\n\n| Demo Model                                                                                                                                                                                                                                                                                                                                        |               Compression Algorithm               |  Backend  |                                Domain                                |\n|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------:|:---------:|:--------------------------------------------------------------------:|\n| [YOLOv8](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov8-optimization)\u003cbr\u003e[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov8-optimization/yolov8-object-detection.ipynb)            |            Post-Training Quantization             | OpenVINO  |  Object Detection,\u003cbr\u003eKeyPoint Detection,\u003cbr\u003eInstance Segmentation   |\n| [EfficientSAM](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/efficient-sam)                                                                                                                                                                                                                                         |            Post-Training Quantization             | OpenVINO  |                          Image Segmentation                          |\n| [Segment Anything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/segment-anything)                                                                                                                                                                                                                            |            Post-Training Quantization             | OpenVINO  |                          Image Segmentation                          |\n| [OneFormer](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/oneformer-segmentation)                                                                                                                                                                                                                                   |            Post-Training Quantization             | OpenVINO  |                          Image Segmentation                          |\n| [CLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/clip-zero-shot-image-classification)                                                                                                                                                                                                                           |            Post-Training Quantization             | OpenVINO  |                            Image-to-Text                             |\n| [BLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/blip-visual-language-processing)                                                                                                                                                                                                                               |            Post-Training Quantization             | OpenVINO  |                            Image-to-Text                             |\n| [Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/latent-consistency-models-image-generation)                                                                                                                                                                                                |            Post-Training Quantization             | OpenVINO  |                            Text-to-Image                             |\n| [Distil-Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/distil-whisper-asr)                                                                                                                                                                                                                                  |            Post-Training Quantization             | OpenVINO  |                            Speech-to-Text                            |\n| [Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/whisper-subtitles-generation)\u003cbr\u003e[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-subtitles-generation/whisper-convert.ipynb) |            Post-Training Quantization             | OpenVINO  |                            Speech-to-Text                            |\n| [MMS Speech Recognition](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/mms-massively-multilingual-speech)                                                                                                                                                                                                           |            Post-Training Quantization             | OpenVINO  |                            Speech-to-Text                            |\n| [LLM Instruction Following](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering)                                                                                                                                                                                                                   |                Weight Compression                 | OpenVINO  |                      NLP, Instruction Following                      |\n| [LLM Chat Bots](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot)                                                                                                                                                                                                                                          |                Weight Compression                 | OpenVINO  |                            NLP, Chat Bot                             |\n\n### Post-Training Quantization and Weight Compression Examples\n\nCompact scripts demonstrating quantization/weight compression and corresponding inference speed boost:\n\n| Example Name                                                                                                                             |              Compression Algorithm               |  Backend   |         Domain         |\n|:-----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------:|:----------:|:----------------------:|\n| [OpenVINO MobileNetV2](./examples/post_training_quantization/openvino/mobilenet_v2/README.md)                                            |            Post-Training Quantization            |  OpenVINO  |  Image Classification  |\n| [OpenVINO YOLO26](./examples/post_training_quantization/openvino/yolo26/README.md)                                                       |            Post-Training Quantization            |  OpenVINO  |    Object Detection    |\n| [OpenVINO YOLOv8 QwAC](./examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/README.md)                   | Post-Training Quantization with Accuracy Control |  OpenVINO  |    Object Detection    |\n| [OpenVINO Anomaly Classification](./examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control |  OpenVINO  | Anomaly Classification |\n| [PyTorch MobileNetV2](./examples/post_training_quantization/torch/mobilenet_v2/README.md)                                                |            Post-Training Quantization            |  PyTorch   |  Image Classification  |\n| [PyTorch SSD](./examples/post_training_quantization/torch/ssd300_vgg16/README.md)                                                        |            Post-Training Quantization            |  PyTorch   |    Object Detection    |\n| [TorchFX Resnet18](./examples/post_training_quantization/torch_fx/resnet18/README.md)                                                    |            Post-Training Quantization            |  TorchFX   |  Image Classification  |\n| [ONNX MobileNetV2](./examples/post_training_quantization/onnx/mobilenet_v2/README.md)                                                    |            Post-Training Quantization            |    ONNX    |  Image Classification  |\n| [ONNX YOLOv8 QwAC](./examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md)                           | Post-Training Quantization with Accuracy Control |    ONNX    |    Object Detection    |\n| [ONNX TinyLlama WC](./examples/llm_compression/onnx/tiny_llama/README.md)                                                                |                Weight Compression                |    ONNX    |           LLM          |\n| [TorchFX TinyLlama WC](./examples/llm_compression/torch_fx/tiny_llama/README.md)                                                         |                Weight Compression                |  TorchFX   |           LLM          |\n| [OpenVINO TinyLlama WC](./examples/llm_compression/openvino/tiny_llama/README.md)                                                        |                Weight Compression                |  OpenVINO  |           LLM          |\n| [OpenVINO TinyLlama WC with HS](./examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md)                               |  Weight Compression with Hyperparameters Search  |  OpenVINO  |           LLM          |\n| [ONNX TinyLlama WC with SE](./examples/llm_compression/onnx/tiny_llama_scale_estimation/README.md)                                       |     Weight Compression with Scale Estimation     |    ONNX    |           LLM          |\n\n### Quantization-Aware Training Examples\n\n| Example Name                                                                        |   Compression Algorithm     | Backend |        Domain        |\n|:------------------------------------------------------------------------------------|:---------------------------:|:-------:|:--------------------:|\n| [PyTorch Resnet18](./examples/quantization_aware_training/torch/resnet18/README.md) | Quantization-Aware Training | PyTorch | Image Classification |\n| [PyTorch Anomalib](./examples/quantization_aware_training/torch/anomalib/README.md) | Quantization-Aware Training | PyTorch | Anomaly Detection    |\n\n\u003ca id=\"third-party-repository-integration\"\u003e\u003c/a\u003e\n\n## Third-party Repository Integration\n\nNNCF may be easily integrated into training/evaluation pipelines of third-party repositories.\n\n### Used by\n\n- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/optimization)\n\n  NNCF is used as a compression backend within the renowned `transformers` repository in HuggingFace Optimum Intel. For instance, the command below exports the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model to OpenVINO format with INT4-quantized weights:\n\n  ```bash\n  optimum-cli export openvino -m meta-llama/Llama-3.2-3B-Instruct --weight-format int4 ./Llama-3.2-3B-Instruct-int4\n  ```\n\n- [Ultralytics](https://docs.ultralytics.com/integrations/openvino)\n\n  NNCF is integrated into the Intel OpenVINO export pipeline, enabling quantization for the exported models.\n\n- [ExecuTorch](https://github.com/pytorch/executorch/blob/main/examples/openvino/README.md)\n\n  NNCF is used as primary quantization framework for the [ExecuTorch OpenVINO integration](https://docs.pytorch.org/executorch/main/build-run-openvino.html).\n\n- [torch.compile](https://docs.pytorch.org/tutorials/prototype/openvino_quantizer.html)\n\n  NNCF is used as primary quantization framework for the [torch.compile OpenVINO integration](https://docs.openvino.ai/2026/openvino-workflow/torch-compile.html).\n\n- [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions)\n\n  NNCF is integrated into OpenVINO Training Extensions as a model optimization backend. You can train, optimize, and\n  export new models based on available model templates as well as run the exported models with OpenVINO.\n\n- [Microsoft Olive](https://github.com/microsoft/olive)\n\n  NNCF is used to quantize OpenVINO IR and ONNX models for the [OpenVINO integration](https://microsoft.github.io/Olive/features/ihv-integration/openvino.html).\n\n\u003ca id=\"installation-guide\"\u003e\u003c/a\u003e\n\n## Installation Guide\n\nFor detailed installation instructions, refer to the [Installation](./docs/Installation.md) guide.\n\nNNCF can be installed as a regular PyPI package via pip:\n\n```bash\npip install nncf\n```\n\nNNCF is also available via [conda](https://anaconda.org/conda-forge/nncf):\n\n```bash\nconda install -c conda-forge nncf\n```\n\nSystem requirements of NNCF correspond to the used backend. System requirements for each backend and\nthe matrix of corresponding versions can be found in [installation.md](./docs/Installation.md).\n\n## NNCF Compressed Model Zoo\n\nList of models and compression results for them can be found at our [NNCF Model Zoo page](./docs/ModelZoo.md).\n\n## Citing\n\n```bi\n@article{kozlov2020neural,\n    title =   {Neural network compression framework for fast model inference},\n    author =  {Kozlov, Alexander and Lazarevich, Ivan and Shamporov, Vasily and Lyalyushkin, Nikolay and Gorbachev, Yury},\n    journal = {arXiv preprint arXiv:2002.08679},\n    year =    {2020}\n}\n```\n\n## Contributing Guide\n\nRefer to the [CONTRIBUTING.md](./CONTRIBUTING.md) file for guidelines on contributions to the NNCF repository.\n\n## Useful links\n\n- [Documentation](./docs)\n- [Examples](./examples)\n- [FAQ](./docs/FAQ.md)\n- [Notebooks](https://github.com/openvinotoolkit/openvino_notebooks#-model-training)\n- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/optimization)\n- [OpenVINO Model Optimization Guide](https://docs.openvino.ai/nncf)\n- [OpenVINO Hugging Face page](https://huggingface.co/OpenVINO#models)\n- [OpenVino Performance Benchmarks page](https://docs.openvino.ai/2026/about-openvino/performance-benchmarks.html)\n\n## Telemetry\n\nNNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the purpose of improving OpenVINO™ tools.\nYou can opt-out at any time by running the following command in the Python environment where you have NNCF installed:\n\n`opt_in_out --opt_out`\n\nMore information available on [OpenVINO telemetry](https://docs.openvino.ai/2026/about-openvino/additional-resources/telemetry.html).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenvinotoolkit%2Fnncf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenvinotoolkit%2Fnncf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenvinotoolkit%2Fnncf/lists"}