Notice bibliographique
Résumé
Highlights New / Updated PTQ Algorithms: Qronos support https://github.com/Xilinx/brevitas/pull/1311 Fixes / improvements to rotation equalization https://github.com/Xilinx/brevitas/pull/1310, https://github.com/Xilinx/brevitas/pull/1312 DDP-like bias correction for SDXL (experimental) https://github.com/Xilinx/brevitas/pull/1342 Improved layer support: Quantization of SDPA without FX https://github.com/Xilinx/brevitas/pull/1299 New export flows: Initial SHARK Export support: https://github.com/Xilinx/brevitas/pull/1300 Initial GGUF Export: https://github.com/Xilinx/brevitas/pull/1291 Improved examples: Qronos examples (paper) https://github.com/Xilinx/brevitas/pull/1311 "Benchmark" experiments for stable diffusion, imagenet https://github.com/Xilinx/brevitas/pull/1281 Post-training model expansion examples (paper) https://github.com/Xilinx/brevitas/pull/1355 Allow signed scales https://github.com/Xilinx/brevitas/pull/1308 QONNX export with dynamo=True https://github.com/Xilinx/brevitas/pull/1234 What's Changed Fix (setup): solve incompatibility between isort and yapf by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1293 Feat (graph/hadamard): 152 had support by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1295 feat (ex/llm): fully parametrise attention quantization by @nickfraser in https://github.com/Xilinx/brevitas/pull/1287 Feat (ex/llm): "auto" dtype by @pablomlago in https://github.com/Xilinx/brevitas/pull/1301 Setup: update transformers version by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1304 Feat (brevitas_examples/llm): quant SDPA without FX by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1299 Fix (brevitas_examples/llm): update README and yaml by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1305 Setup: temporary pin pytest by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1307 Feat (brevitas_examples/llm): configurable expansion step by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1280 Versioning support in documentation by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1298 Feat (graph/rotate): improve R2 region in SDPA by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1310 Docs: improve docs build by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1314 Fix (graph/rotation): rotation on subset of channels for SDPA by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1312 Feat (brevitas_examples/llm): GGUF export by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1291 Setup: bump torch version by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1205 Fix (ex/imagenet): add forward pass in imagenet ptq example by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1316 Feat (qronos): initial implementation of Qronos by @i-colbert in https://github.com/Xilinx/brevitas/pull/1311 Fix (graph/equalize): correct class check during rotation merging by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1317 docs (core): Typo fix in docstring by @nickfraser in https://github.com/Xilinx/brevitas/pull/1318 Fix (llm): removing duplicate set_seed function by @i-colbert in https://github.com/Xilinx/brevitas/pull/1319 Fix (ex/common): save scales during optimization by @pablomlago in https://github.com/Xilinx/brevitas/pull/1313 Fix (llm): compatibility with non-uniform RMSNorm shapes by @i-colbert in https://github.com/Xilinx/brevitas/pull/1324 Fix (graph/gpxq): fix memory leak with weight_orig by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1325 Shark LLM export by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1300 Feat (scaling): rescaled min-max scaling and zero point by @i-colbert in https://github.com/Xilinx/brevitas/pull/1320 Fix (gguf): derived modify_tensors() returns generator by @i-colbert in https://github.com/Xilinx/brevitas/pull/1329 Fix (gpxq): device management of weight_orig for GPxQ by @i-colbert in https://github.com/Xilinx/brevitas/pull/1330 Fix (gguf): resolving zero point permutation issue with LlamaModel by @i-colbert in https://github.com/Xilinx/brevitas/pull/1332 Feat (examples): refactor imagenet and stable_diffusion entrypoints by @pablomlago in https://github.com/Xilinx/brevitas/pull/1281 Feat: skipping rotation optimization with load_checkpoint by @i-colbert in https://github.com/Xilinx/brevitas/pull/1331 Feat (core): Remove assumptions on positiveness of scales by @pablomlago in https://github.com/Xilinx/brevitas/pull/1308 Fix (export/qonnx): Add export support with dynamo=True by @nickfraser in https://github.com/Xilinx/brevitas/pull/1234 Fix (core/ops_ste): preserve dtype during clamp by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1340 Fix (eq/rotation): find value if passed as kwarg by @nickfraser in https://github.com/Xilinx/brevitas/pull/1338 Feat (ex): tests Stable Diffusion and ImageNet by @pablomlago in https://github.com/Xilinx/brevitas/pull/1339 Feat (ex/sdxl): DDP-like bias correction for SDXL by @pablomlago in https://github.com/Xilinx/brevitas/pull/1342 Feat (graph): Minor refactoring layerwise_layer_handler by @pablomlago in https://github.com/Xilinx/brevitas/pull/1335 Fix (torch_utils): remove deprecated functions by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1344 Fix (setup): test against latest 2.1 torch by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1348 Rotation fix by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1334 Docs (qronos): adding docs and configs by @i-colbert in https://github.com/Xilinx/brevitas/pull/1326 Feat: Added ONNX export to BNN-PYNQ example by @nickfraser in https://github.com/Xilinx/brevitas/pull/916 Fix (deps): set accelerate<1.10 by @nickfraser in https://github.com/Xilinx/brevitas/pull/1352 Fix (copyright): Fix some missing copyright headers by @nickfraser in https://github.com/Xilinx/brevitas/pull/1353 Fix (ex/llm/benchmark): import error by @nickfraser in https://github.com/Xilinx/brevitas/pull/1356 Setup: temporarily pin diffusers version by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1360 Fix (core/scaling): handle edge cases with signed scale by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1347 Fix(core/stochastic_round): adjust stochastic round device by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1359 Feat (papers): expansion paper configs by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1355 Fix (docs): correct link to docs in initial README by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/1361 Fix (setup.py): Update author and contact in setup.py by @nickfraser in https://github.com/Xilinx/brevitas/pull/1365 Docs (README): Update maximum PyTorch version by @nickfraser in https://github.com/Xilinx/brevitas/pull/1366 Docs (getting started): Typo fix in example by @nickfraser in https://github.com/Xilinx/brevitas/pull/1367 requirements: Updated PyTorch, python versions by @nickfraser in https://github.com/Xilinx/brevitas/pull/1370 Feat (core/scaling): Add option to restrict the output of (scale / threshold) by @nickfraser in https://github.com/Xilinx/brevitas/pull/1369 deps (ex) update accelerate version by @nickfraser in https://github.com/Xilinx/brevitas/pull/1371 Full Changelog: https://github.com/Xilinx/brevitas/compare/v0.12.0...v0.12.1
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,364 | 0,398 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».