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| ## 2.18.0 | |
| * Enable int8 inference without `.weight` attribute | |
| ## 2.17.2 | |
| * Update push_to_hf_hub | |
| ## 2.17.0 | |
| * Add int8 support | |
| * Update notebook demo | |
| * Refactor zero-shot classification code | |
| ## 2.16.2 | |
| * Fixes for context_length and vocab_size attributes | |
| ## 2.16.1 | |
| * Fixes for context_length and vocab_size attributes | |
| * Fix --train-num-samples logic | |
| * Add HF BERT configs for PubMed CLIP model | |
| ## 2.16.0 | |
| * Add improved g-14 weights | |
| * Update protobuf version | |
| ## 2.15.0 | |
| * Add convnext_xxlarge weights | |
| * Fixed import in readme | |
| * Add samples per second per gpu logging | |
| * Fix slurm example | |
| ## 2.14.0 | |
| * Move dataset mixtures logic to shard level | |
| * Fix CoCa accum-grad training | |
| * Safer transformers import guard | |
| * get_labels refactoring | |
| ## 2.13.0 | |
| * Add support for dataset mixtures with different sampling weights | |
| * Make transformers optional again | |
| ## 2.12.0 | |
| * Updated convnext configs for consistency | |
| * Added input_patchnorm option | |
| * Clean and improve CoCa generation | |
| * Support model distillation | |
| * Add ConvNeXt-Large 320x320 fine-tune weights | |
| ## 2.11.1 | |
| * Make transformers optional | |
| * Add MSCOCO CoCa finetunes to pretrained models | |
| ## 2.11.0 | |
| * coca support and weights | |
| * ConvNeXt-Large weights | |
| ## 2.10.1 | |
| * `hf-hub:org/model_id` support for loading models w/ config and weights in Hugging Face Hub | |
| ## 2.10.0 | |
| * Added a ViT-bigG-14 model. | |
| * Added an up-to-date example slurm script for large training jobs. | |
| * Added a option to sync logs and checkpoints to S3 during training. | |
| * New options for LR schedulers, constant and constant with cooldown | |
| * Fix wandb autoresuming when resume is not set | |
| * ConvNeXt `base` & `base_w` pretrained models added | |
| * `timm-` model prefix removed from configs | |
| * `timm` augmentation + regularization (dropout / drop-path) supported | |
| ## 2.9.3 | |
| * Fix wandb collapsing multiple parallel runs into a single one | |
| ## 2.9.2 | |
| * Fix braceexpand memory explosion for complex webdataset urls | |
| ## 2.9.1 | |
| * Fix release | |
| ## 2.9.0 | |
| * Add training feature to auto-resume from the latest checkpoint on restart via `--resume latest` | |
| * Allow webp in webdataset | |
| * Fix logging for number of samples when using gradient accumulation | |
| * Add model configs for convnext xxlarge | |
| ## 2.8.2 | |
| * wrapped patchdropout in a torch.nn.Module | |
| ## 2.8.1 | |
| * relax protobuf dependency | |
| * override the default patch dropout value in 'vision_cfg' | |
| ## 2.8.0 | |
| * better support for HF models | |
| * add support for gradient accumulation | |
| * CI fixes | |
| * add support for patch dropout | |
| * add convnext configs | |
| ## 2.7.0 | |
| * add multilingual H/14 xlm roberta large | |
| ## 2.6.1 | |
| * fix setup.py _read_reqs | |
| ## 2.6.0 | |
| * Make openclip training usable from pypi. | |
| * Add xlm roberta large vit h 14 config. | |
| ## 2.5.0 | |
| * pretrained B/32 xlm roberta base: first multilingual clip trained on laion5B | |
| * pretrained B/32 roberta base: first clip trained using an HF text encoder | |
| ## 2.4.1 | |
| * Add missing hf_tokenizer_name in CLIPTextCfg. | |
| ## 2.4.0 | |
| * Fix #211, missing RN50x64 config. Fix type of dropout param for ResNet models | |
| * Bring back LayerNorm impl that casts to input for non bf16/fp16 | |
| * zero_shot.py: set correct tokenizer based on args | |
| * training/params.py: remove hf params and get them from model config | |
| ## 2.3.1 | |
| * Implement grad checkpointing for hf model. | |
| * custom_text: True if hf_model_name is set | |
| * Disable hf tokenizer parallelism | |
| ## 2.3.0 | |
| * Generalizable Text Transformer with HuggingFace Models (@iejMac) | |
| ## 2.2.0 | |
| * Support for custom text tower | |
| * Add checksum verification for pretrained model weights | |
| ## 2.1.0 | |
| * lot including sota models, bfloat16 option, better loading, better metrics | |
| ## 1.2.0 | |
| * ViT-B/32 trained on Laion2B-en | |
| * add missing openai RN50x64 model | |
| ## 1.1.1 | |
| * ViT-B/16+ | |
| * Add grad checkpointing support | |
| * more robust data loader | |