v0.4.0

New Features (highlights)

• Streaming multipack for continued pre-training
• Mistral & Mixtral support
• Simplified Multipack for Mistral, Falcon, Qwen2, and Phi
• DPO/IPO/KTO-pairs RL-training support via trl
• Improve BatchSampler for multipack support, allows for resume from checkpointing, shuffling data each epoch
• bf16: auto support
• add MLFlow support
• save YAML configs to WandB
• save predictions during evals to WandB
• more tests! more smoke tests for smol model training
• NEFTune support

What's Changed

• document that packaging needs to be installed before flash-attn by @winglian in #559
• Fix pretraining with iterable/streaming Dataset by @jphme in #556
• Add training callback to send predictions to WandB table by @Glavin001 in #521
• fix wandb so mypy doesn't complain by @winglian in #562
• check for the existence of the default accelerate config that can create headaches by @winglian in #561
• add optimization for group-by-len by @winglian in #563
• gracefully handle length feature used for group by by @winglian in #565
• improve how we setup eval/save strategies and steps by @winglian in #547
• let hf trainer handle torch compile by @winglian in #516
• Model parallel by @winglian in #538
• fix save_steps so it doesn't get duplicated by @winglian in #567
• set auto for other params that hf trainer sets for ds. include zero1 json by @winglian in #570
• remove columns after tokenizing for pretraining by @winglian in #571
• mypy wandb ignore by @winglian in #572
• Phi examples by @winglian in #569
• e2e testing by @winglian in #574
• E2e device cuda by @winglian in #575
• E2e passing tests by @winglian in #576
• refactor scripts/finetune.py into new cli modules by @winglian in #550
• update support matrix with btlm and phi by @winglian in #579
• prevent cli functions from getting fired on import by @winglian in #581
• Fix Codellama examples by @Kimiko-AI in #582
• support custom field for completion from yml by @winglian in #580
• Feat(doc): Add features to doc by @NanoCode012 in #583
• Support Sample packing for phi arch by @winglian in #586
• don't resize embeddings if it's already large enough by @winglian in #577
• Enable full (non-sharded) model saving with SHARDED_STATE_DICT by @jphme in #584
• make phi training work with Loras by @winglian in #588
• optionally configure sample packing for evals by @winglian in #589
• don't add position_ids for evals when not using eval sample packing by @winglian in #591
• gather/broadcast the max value of the packing efficiency automatically by @winglian in #463
• Feat(data): Allow loading local csv and text by @NanoCode012 in #594
• add bf16 check by @winglian in #587
• btlm and falcon monkey patches for flash attn by @winglian in #566
• minor tweaks to simplify by @winglian in #597
• Fix for check with cfg and merge_lora by @winglian in #600
• improve handling for empty text on the tokenization step by @winglian in #502
• more sane defaults for openllama 3b used for quickstarts by @winglian in #602
• update dockerfile to not build evoformer since it fails the build by @winglian in #607
• Delete duplicate lines in models.py by @bofenghuang in #606
• support to disable exllama for gptq by @winglian in #604
• Update requirements.txt - Duplicated package by @Psancs05 in #610
• Only run tests when a change to python files is made by @maximegmd in #614
• Create multi-node.md by @maximegmd in #613
• fix distributed devices by @maximegmd in #612
• ignore wandb to resolve isort headaches by @winglian in #619
• skip the gpu memory checks if the device is set to 'auto' by @winglian in #609
• let MAX_JOBS use the default since we're not resource constrained on our self-hosted runners by @winglian in #427
• run eval on the first step to get a baseline by @winglian in #617
• split completion text to sequence_len by @winglian in #616
• misc fixes to add gptq tests by @winglian in #621
• chore(callback): Remove old peft saving code by @NanoCode012 in #510
• update README w deepspeed info by @winglian in #605
• create a model card with axolotl badge by @winglian in #624
• better handling and logging of empty sharegpt turns by @winglian in #603
• tweak: improve base builder for smaller layers by @maximegmd in #500
• Feat(doc): Add eval_sample_packing to doc by @NanoCode012 in #625
• Fix: Fail bf16 check when running on cpu during merge by @NanoCode012 in #631
• default model changed by @mhenrichsen in #629
• Added quotes to the pip install -e command in the documentation to fix an incompatibility … by @Nan-Do in #632
• Feat: Add support for upstream FA2 by @NanoCode012 in #626
• eval_table isn't quite stable enough to be in default llama configs by @winglian in #637
• attention_mask not needed for training by @winglian in #642
• update for recent transformers updates by @winglian in #636
• use fastchat conversations template by @winglian in #578
• skip some flash attn patches unless explicitly enabled by @winglian in #643
• Correct typos in datasets.py by @felixonmars in #639
• Fix bug in dataset loading by @ethanhs in #284
• Warn users to login to HuggingFace by @Napuh in #645
• Mistral flash attn packing by @winglian in #646
• Fix(cfg): Add validation for save_strategy and eval_strategy by @NanoCode012 in #633
• Feat: Add example for Mistral by @NanoCode012 in #644
• Add mistral/README.md by @adarshxs in #647
• fix for flash attn w mistral w/o sammple packing by @winglian in #648
• don't strip the prompt for check since we don't strip to tokenize anymore by @winglian in #650
• add support for defined train split by @winglian in #654
• Fix bug when using pretokenized datasets by @ein-ich in #652
• Make dataset_processes configurable by @corbt in #651
• add mistral e2e tests by @winglian in #649
• removed duplicate on requirements.txt by @Napuh in #661
• make sure we also run CI tests when requirements.txt changes by @winglian in #663
• prepared dataset caching, other misc fixes by @winglian in #665
• remove patch fix for phi by @winglian in #664
• refactor to set eval_batch_size earlier if unset, so we can warn if mismatched by @winglian in #662
• Feat: Add config yaml to section for reprod in bug-report.yaml by @NanoCode012 in #667
• Feat: Allow usage of native Mistral FA when no sample_packing by @NanoCode012 in #669
• chore: Clean up repetitive model kwargs by @NanoCode012 in #670
• Fix(version): Update FA to work with Mistral SWA by @NanoCode012 in #673
• Fix(tokenizer): Set rstrip,lstrip,norm to False by @NanoCode012 in #678
• Fix: Future deprecation warning with use_auth_token by @NanoCode012 in #680
• Feat: Set WORKDIR to /workspace/axolotl by @NanoCode012 in #679
• Fix: ValueError when FA + Mistral when padding_side=right by @NanoCode012 in #681
• flash_attention + sample packing for stablelm 3b by @winglian in #671
• Adding qlora config for Mistral by @TokenBender in #675
• Fix: Higher vram usage for mistral and sample_packing by @NanoCode012 in #691
• fix multiline for docker by @winglian in #694
• update mistral lr, sample pack by @mhenrichsen in #693
• apex not needed as amp is part of pytorch by @winglian in #696
• add docker images for pytorch 2.10 by @winglian in #697
• fix unneeded space by @mhenrichsen in #699
• Update README with some explanations by @seungduk-yanolja in #700
• Get qlora mistral-7b fine tuning working on a single 4090 by @lukemarsden in #708
• fix(doc): Add note on inference w sample packing by @NanoCode012 in #712
• Fix: lowercase True values in config by @atgctg in #713
• fix(doc): update default doc according to arg by @NanoCode012 in #714
• Save Axolotl config as WandB artifact by @jphme in #716
• improve handling of the prepared ds path and other cfg defaults by @winglian in #701
• fix pytorch 2.1.0 build, add multipack docs by @winglian in #722
• add noisy embedding by @maximegmd in #721
• pin xformers >= 0.0.22 by @winglian in #724
• misc sharegpt fixes by @winglian in #723
• workaround for installing xformers w torch 2.1.0 by @winglian in #725
• tweak for xformers install w pytorch 2.1.0 by @winglian in #727
• fixes for alpaca w chatml, and don't include attention_mask w mistral for flash attention by @winglian in #728
• Clarify custom format example by @casper-hansen in #729
• Mistral: Sliding Window Attention with Flash Attention and Sample Packing by @casper-hansen in #732
• badge by @mhenrichsen in #739
• catch ConnectionError when checking dataset from HuggingFace by @Napuh in #743
• Fix(model): Linear detected and added to target module with rope linear by @NanoCode012 in #738
• improve: Enhance code readability of prompt_tokenizers.py by @seungduk-yanolja in #707
• add a latest tag for regular axolotl image, cleanup extraneous print statement by @winglian in #746
• Fix DeepSpeed Zero 3 Saving by @tokestermw in #709
• chore: bump transformers to v4.34.1 to fix tokenizer issue by @NanoCode012 in #745
• add to docs by @winglian in #703
• Implement fused modules by @casper-hansen in #747
• remove lora fused packing test by @winglian in #758
• Fix: eval table conflict with eval_sample_packing by @NanoCode012 in #769
• Fix: Cannot tokenize with bf16 and on cpu by @NanoCode012 in #766
• Hotfix for fused QKV not saving the trained weights of o_proj by @casper-hansen in #762
• convert exponential notation lr to floats by @winglian in #771
• Fix: Warn when fullfinetune without adapter by @NanoCode012 in #770
• simplify by removing duplicate base_model_config by @winglian in #772
• disable eval table w sample packing in examples by @winglian in #778
• refactor setup trainer so we can add more hooks by @winglian in #773
• chore: refactor truthy check and fix mypy by @NanoCode012 in #780
• chore(readme): Improve documentation on conversation field by @NanoCode012 in #782
• Threaded MultipackDistributedDataloader with prefetched samples by @casper-hansen in #759
• Create preprocess CLI by @casper-hansen in #785
• Add docker advanced instruction to README by @gordicaleksa in #792
• Fix Deepspeed Zero3 Config by @teknium1 in #791
• Update to adapt to sharegpt datasets with "assistant" rather than "gp… by @MilesQLi in #774
• fix eval_steps to be a sane default by @winglian in #797
• refactor neft patch to be more re-usable similar to trl's impl by @winglian in #796
• fix(config): Set eos/bos to tokenizer if different by @NanoCode012 in #801
• feat(doc): add dummyoptim faq fix by @NanoCode012 in #802
• fix(tokenizer): update log order after update by @NanoCode012 in #806
• fix model parallel by @winglian in #816
• fix: pin autogptq by @NanoCode012 in #818
• update table for rwkv4 support, fix process count for dataset by @winglian in #822
• Feat: Added Gradio support by @Stillerman in #812
• Dockerfile: add deepspeed-kernels dependency for deepspeed>=0.12.0 by @fpreiss in #827
• cleanup verbosity a bit by @winglian in #799
• make sure to cleanup tmp output_dir for e2e tests by @winglian in #831
• multipack w batch sampler by @winglian in #795
• don't compile deepspeed or bitsandbytes from source by @winglian in #837
• Pin optimum package by @brthor in #838
• cleanup the old multipack dataloader by @winglian in #841
• include the suffix modified string in ascii art by @fpreiss in #852
• feat(doc): add more info on train_on_split by @NanoCode012 in #855
• chore(doc): Separate section on runpod by @NanoCode012 in #860
• various bugfixes by @winglian in #856
• adds llama and mistral dropout support by @winglian in #858
• multipack len should use max, not min by @winglian in #863
• Docs: add instructions to 1-click launching on public clouds by @concretevitamin in #862
• Update data.py for signature generation by @MilesQLi in #851
• lint fix that didn't get caught by linter by @winglian in #866
• make docker command more robust by @winglian in #861
• add e2e tests for checking functionality of resume from checkpoint by @winglian in #865
• allow overriding of model_config parameters from the YML by @winglian in #853
• Feat: Add dataset loading from S3, GCS by @NanoCode012 in #765
• try #2: pin hf transformers and accelerate to latest release, don't reinstall pytorch by @winglian in #867
• don't train if eval split is too small by @winglian in #873
• Phi update 202311 by @winglian in #876
• Install from git url by @msaroufim in #874
• fix: revert local dir dataset load by @NanoCode012 in #878
• chore(doc): Add info on changing role in sharegpt by @NanoCode012 in #886
• Feat: Add warmup_ratio by @NanoCode012 in #893
• fix: warning should not show if eval_batch_size not provided by @NanoCode012 in #896
• Feat: Add Qwen by @NanoCode012 in #894
• update datasets version to cut down the warnings due to pyarrow arg change by @winglian in #897
• fix: remove FA for qwen examples by @NanoCode012 in #900
• Determine FSDP/deepspeed settings on device select. by @kallewoof in #883
• ensure merged model matches the training dtype by @winglian in #902
• fix for qwen w lora by @winglian in #906
• Remove lr scheduler in DeepSpeed config to avoid conflict by @Haoxiang-Wang in #909
• feature: loss watchdog for terminating training runs that are failing by @kallewoof in #899
• Feat(wandb): Refactor to be more flexible by @NanoCode012 in #767
• Support device_map=sequential & max_memory config parameters by @brthor in #903
• feat: add check for quantized model by @NanoCode012 in #913
• Pin flash-attn to 2.3.3 by @casper-hansen in #919
• fix(tokenizer): handle fast tokenizer properly for bos/eos by @NanoCode012 in #914
• support for mamba by @winglian in #915
• fixing prompt template of chatml by removal of linebreak by @timothylimyl in #922
• Mixtral multipack by @winglian in #928
• update to latest transformers for mixstral support by @winglian in #929
• Mixtral: More correct MoE, lower loss by @casper-hansen in #932
• Update requirements.txt (fschat==0.2.34) by @tokestermw in #940
• Mixtral official by @winglian in #942
• Respect sequence_len in config for type: llama2_chat by @hamelsmu in #926
• new evals_per_epoch and saves_per_epoch to make things cleaner by @winglian in #944
• More hints on what to do with CUDA Out of memory errors by @jooray in #925
• fix: remove excessive newlines in system prompt(s) for alpaca by @kallewoof in #936
• Flash attn hotfix by @winglian in #951
• Fix Deepspeed loading by @winglian in #950
• fix: switch to using the HuggingFace Transformers NEFT implementation by @kallewoof in #941
• Add docs by @hamelsmu in #947
• Fix prompt assembly for llama by @hamelsmu in #952
• update transformers to fix checkpoint saving by @dumpmemory in #963
• update to latest nccl in docker image by @winglian in #965
• fix for build for nccl in dockerfile by @winglian in #970
• fix: add lr scheduler kwargs to Trainer by @NanoCode012 in #972
• Update README.md by @eltociear in #966
• Dockerfile torch fix by @winglian in #987
• fix mistral prompt assembly by @hamelsmu in #982
• Feat: Warns to add to modules_to_save when adding tokens or switching special_tokens by @NanoCode012 in #787
• Add tests to Docker by @hamelsmu in #993
• change val size by @mhenrichsen in #992
• chore: Update transformers to latest by @NanoCode012 in #986
• support for cuda 12.1 by @winglian in #989
• set output_router_logits for mixtral config: by @winglian in #995
• Add an example config for finetuning a 34B model on a 24GB GPU by @evangriffiths in #1000
• FEAT: add tagging support to axolotl by @younesbelkada in #1004
• Set eval_sample_packing to false in mistral config.yaml by @kmsydney in #1003
• add config to model card by @hamelsmu in #1005
• remove landmark attn and xpos rope implementations by @winglian in #1010
• [Docs] Nit: clarify what inference is by @hamelsmu in #1012
• [Docs] Nit: Remind people to auth to wandb if they are going to use it by @hamelsmu in #1013
• feat: remove need to add load_in* during merge by @NanoCode012 in #1017
• feat: expose bnb kwargs by @NanoCode012 in #1018
• add ultrachat prompt strategies by @winglian in #996
• [WandB] Push axolotl config to top level wandb files by @hamelsmu in #1014
• Adds chat templates by @mhenrichsen in #1022
• Fix: bf16 support for inference by @taziksh in #981
• use recommended setting for use_reentrant w gradient checkpointing by @winglian in #1021
• added tiny llama examples for lora and qlora by @tdolan21 in #1027
• chore(readme): update instruction to set config to load from cache by @NanoCode012 in #1030
• [Docs] delete unused cfg value lora_out_dir by @hamelsmu in #1029
• fix: lint by @NanoCode012 in #1037
• chore(config): clean up old log for Qwen by @NanoCode012 in #1034
• bump transformers and update attention class map name by @winglian in #1023
• Added chatglm3 conversation type for training models like TinyLLama by @xaviviro in #1036
• fix HF model card upload for PEFT models by @hamelsmu in #1043
• Clean Up LorA Merge by @hamelsmu in #1044
• feature: better device mapping for large models by @kallewoof in #918
• feat: always push checkpoint to hub if set by @NanoCode012 in #1049
• Update tests-docker.yml by @hamelsmu in #1052
• streaming multipack for pretraining dataset by @jinwonkim93 in #959
• Simplify Docker Unit Test CI by @hamelsmu in #1055
• Phi2 rewrite by @winglian in #1058
• Efficiently get the length of the tokenized docs by @RicardoDominguez in #1063
• Sponsors by @winglian in #1065
• Update FUNDING.yml for Kofi link by @winglian in #1067
• fix: torch_dtype mistral default to fp32 by @NanoCode012 in #1050
• Cosine learning rate schedule - minimum learning rate by @RicardoDominguez in #1062
• fix double eos token for chatml by @winglian in #1054
• Add: mlflow for experiment tracking by @JohanWork in #1059
• update peft to 0.7.0 by @mtenenholtz in #1073
• paired kto support by @winglian in #1069
• Separate AutoGPTQ dep to pip install -e .[auto-gptq] by @casper-hansen in #1077
• attempt to also run e2e tests that needs gpus by @winglian in #1070
• Update FUNDING.yml with bitcoin by @winglian in #1079
• swap the data collator for evals if not using sample packing by @winglian in #1076
• be more robust about checking embedding modules for lora finetunes by @winglian in #1074
• fix: train_on_inputs: true ignored for sharegpt by @NanoCode012 in #1045
• update sharegpt conversations when chatml chat template is set by @winglian in #1075
• additional logging to get maximum token length of a sequence in the dataset by @winglian in #1066
• pin accelerate for deepspeed fix by @winglian in #1080
• fix: warn user to install mamba_ssm package by @NanoCode012 in #1019
• use tags again for test image, only run docker e2e after pre-commit checks by @winglian in #1081
• optimize calculation of cu_seqlens from position_ids by @winglian in #1084
• add python 3.11 to the matrix for unit tests by @winglian in #1085
• Remove fused-dense-lib from requirements.txt by @casper-hansen in #1087
• misc fixes from #943 by @winglian in #1086
• add gptneox embeddings, fix phi2 inputs, also fix the casting by @winglian in #1083
• Add Debugging Guide by @hamelsmu in #1089
• Fix debugging.md by @hamelsmu in #1091
• feat: enable trl's autounwrap by @NanoCode012 in #1060
• Fix broken pypi.yml by @msaroufim in #1099
• Update README.md by @hamelsmu in #1103
• Add section for debugging with Docker by @hamelsmu in #1104
• Add link on README to Docker Debugging by @hamelsmu in #1107
• keep gate in fp32 for loras by @winglian in #1105
• Fix debugging video by @hamelsmu in #1111
• Disable caching on --disable_caching in CLI by @casper-hansen in #1110
• Reverse caching PR by @casper-hansen in #1115
• Enable or disable bf16 support based on availability by @simhallq in #1116
• update PR template so we can capture twitter or discord handles by @winglian in #1121
• pin model_revision for phi2 by @winglian in #1123
• fix(readme): clarify custom user prompt [no-ci] by @NanoCode012 in #1124
• Add layers_to_transform for lora_config by @xzuyn in #1118
• Agnostic cloud gpu docker image and Jupyter lab by @winglian in #1097
• Preprocess dataset size fix by @winglian in #1131
• fix(preprocess): Make sure dataset not loaded from cache when using preprocess cli by @NanoCode012 in #1136
• fix bf16 check when preprocessing data by @winglian in #1140
• Add shifted sparse attention by @joecummings in #973
• Multipack simplify for Mixtral by @winglian in #1142
• Fix link for Minotaur model by @joecummings in #1146
• Dockerfile cloud ports by @winglian in #1148
• fix check for env var by @winglian in #1151
• feat(dataset): add config to keep processed dataset in memory by @NanoCode012 in #1152
• Deprecate max packed sequence len by @winglian in #1141
• make sure the model config loader respects the model_revision too by @winglian in #1160
• Qwen2 by @winglian in #1166
• jupyter lab fixes by @winglian in #1139
• set fp16 to false if bf16, update bf16: auto in example YAMLs by @winglian in #1122
• Add mlflow callback for pushing config to mlflow artifacts by @JohanWork in #1125
• improve vram use w gradient checkpointing by @winglian in #1167
• Vram fix attempt by @winglian in #1164
• add commit message option to skip docker image builds in ci by @winglian in #1168
• Falcon embeddings by @winglian in #1149
• support for explicit test_dataset definition for evals by @winglian in #786
• Add desc to map/filter by @casper-hansen in #1162
• Feat(test): Add tests for alpaca chatml prompt tokenizer by @JohanWork in #1088
• DPO cleanup by @winglian in #1126
• Update README.md by @singhay in #1169
• Fine-Tuning Mistral-7b for Real-World Chatbot Applications Using Axolotl (Lora used) by @Tilemachoc in #1155
• don't fail if can't cast weights due to offload when merging by @winglian in #1172
• update docs by @winglian in #1176
• Phi2 multipack by @winglian in #1173
• DPO fixes v2 by @winglian in #1174
• Docs: RLHF Update after cleanup by @AlekseyKorshuk in #1178
• Add support for offline mode with HF_HUB_OFFLINE envvar by @JamesHWade in #1182
• Fix do_merge_lora raises an Exception in transformers v4.37.0 by @tisorlawan in #1184
• report min lenght of tokenized data by @winglian in #1186
• more dpo fixes for dataset loading and docs by @winglian in #1185
• upgrade deepspeed to 0.13.1 for mixtral fixes by @winglian in #1189
• Standardize system prompt format for AlpacaPrompter (instruct case) by @sadaisystems in #1190
• Mixtral fixes 20240124 by @winglian in #1192
• prepare for release v0.4.0 by @winglian in #1175

New Contributors

• @Kimiko-AI made their first contribution in #582
• @bofenghuang made their first contribution in #606
• @Psancs05 made their first contribution in #610
• @Nan-Do made their first contribution in #632
• @felixonmars made their first contribution in #639
• @Napuh made their first contribution in #645
• @adarshxs made their first contribution in #647
• @ein-ich made their first contribution in #652
• @corbt made their first contribution in #651
• @TokenBender made their first contribution in #675
• @seungduk-yanolja made their first contribution in #700
• @lukemarsden made their first contribution in #708
• @atgctg made their first contribution in #713
• @casper-hansen made their first contribution in #729
• @tokestermw made their first contribution in #709
• @gordicaleksa made their first contribution in #792
• @MilesQLi made their first contribution in #774
• @Stillerman made their first contribution in #812
• @fpreiss made their first contribution in #827
• @brthor made their first contribution in #838
• @concretevitamin made their first contribution in #862
• @msaroufim made their first contribution in #874
• @kallewoof made their first contribution in #883
• @Haoxiang-Wang made their first contribution in #909
• @timothylimyl made their first contribution in #922
• @hamelsmu made their first contribution in #926
• @jooray made their first contribution in #925
• @dumpmemory made their first contribution in #963
• @eltociear made their first contribution in #966
• @evangriffiths made their first contribution in #1000
• @younesbelkada made their first contribution in #1004
• @kmsydney made their first contribution in #1003
• @taziksh made their first contribution in #981
• @tdolan21 made their first contribution in #1027
• @xaviviro made their first contribution in #1036
• @jinwonkim93 made their first contribution in #959
• @RicardoDominguez made their first contribution in #1063
• @JohanWork made their first contribution in #1059
• @mtenenholtz made their first contribution in #1073
• @simhallq made their first contribution in #1116
• @xzuyn made their first contribution in #1118
• @joecummings made their first contribution in #973
• @singhay made their first contribution in #1169
• @Tilemachoc made their first contribution in #1155
• @AlekseyKorshuk made their first contribution in #1178
• @JamesHWade made their first contribution in #1182
• @tisorlawan made their first contribution in #1184
• @sadaisystems made their first contribution in #1190

Full Changelog: v0.3.0...v0.4.0