* Prepare for transformers v5 upgrade
* fix hf cli
* update for hf hub changes
* fix tokenizer apply_chat_template args
* remap include_tokens_per_second
* fix tps
* handle migration for warmup
* use latest hf hub
* Fix scan -> ls
* fix import
* fix for renaming of mistral common tokenizer -> backend
* update for fixed tokenziation for llama
* Skip phi35 tests for now
* remove mistral patch fixed upstream in huggingface/transformers#41439
* use namespacing for patch
* don't rely on sdist for e2e tests for now
* run modal ci without waiting too
* Fix dep for ci
* fix imports
* Fix fp8 check
* fsdp2 fixes
* fix version handling
* update fsdp version tests for new v5 behavior
* Fail multigpu tests after 3 failures
* skip known v5 broken tests for now and cleanup
* bump deps
* unmark skipped test
* re-enable test_fsdp_qlora_prequant_packed test
* increase multigpu ci timeout
* skip broken gemma3 test
* reduce timout back to original 120min now that the hanging test is skipped
* fix for un-necessary collator for pretraining with bsz=1
* fix: safe_serialization deprecated in transformers v5 rc01 (#3318)
* torch_dtype deprecated
* load model in float32 for consistency with tests
* revert some test fixtures back
* use hf cache ls instead of scan
* don't strip fsdp_version
more fdsp_Version fixes for v5
fix version in fsdp_config
fix aliasing
fix fsdp_version check
check fsdp_version is 2 in both places
* Transformers v5 rc2 (#3347)
* bump dep
* use latest fbgemm, grab model config as part of fixture, un-skip test
* import AutoConfig
* don't need more problematic autoconfig when specifying config.json manually
* add fixtures for argilla ultrafeedback datasets
* download phi4-reasoning
* fix arg
* update tests for phi fast tokenizer changes
* use explicit model types for gemma3
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* fix: AutoModelForVision2Seq -> AutoModelForImageTextToText
* chore: remove duplicate
* fix: attempt fix gemma3 text mode
* chore: lint
* ga release of v5
* need property setter for name_or_path for mistral tokenizer
* vllm not compatible with transformers v5
* setter for chat_template w mistral too
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
* upgrade transformers to 4.57.5
* explicitly set versions for fbgemm-gpu
* handle index url for cuda version
* explicitly set cu version for fbgemm deps, skip for 130
* cu suffix not needed on version if using whl subpath
* install xformers in the base docker image
* install numba and numpy first
* set CUDA_HOME for xformers install
* Set cuda home env
* don't install xformers by default on aarch64/arm64
* fix syntax for secrets in gha yaml
* setup env for uv too
* arm64 for base uv too
* don't build causal-conv1d or mamba for arm64 and use arm64 wheels
* fix dockerfile syntax
* fix shell syntax
* upgrade dependencies
* don't use reset sessions
* downgrade transformers, upgrade other deps
* upgrade bnb to 0.49.0
* restore s3 cache
* explicit use local files w hub
* decompress and strip top level dir
* use 2 levels for strip components
* try to preserve permissions for symlinks
* use updated tar
* fix#3293 for distributed
* downgrade bnb
* fast fail after 4
* fix total tokens device
* patch accelerate CP/SP (#3309)
---------
Co-authored-by: salman <salman.mohammadi@outlook.com>
* build examples readmes with quarto
* chore: formatting
* feat: dynamic build docs
* feat: add more model guides
* chore: format
* fix: collapse sidebar completely to have space for model guides
* fix: security protection for generated qmd
* fix: adjust collapse level, add new models, update links
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* compute loss only if training
* save total_tokens for checkpiont
* check if string
* refactor total_tokens/ num_tokens
* refactor 2
* rplc trainable_step/trian_per_sec_per_gpu
* lint + log trainable/tokens
* consolidate it in the callback.
* test for total_tokes aftr remuse
* check if tokenstate exist after ckpt
---------
Co-authored-by: Ved <ved.work2024@gmail.com>
* feature: raise on long sequence drop
It is sometimes not desired that sequences are silently dropped from the dataset, especially when the dataset has been carefully crafted and pre-fitted for the training context. This would then suggest that an error occurred somewhere in the process. This feature adds a third value for excess_length_strategy called 'raise', which will raise a ValueError if a sequence is encountered that is too long and would have normally been dropped/truncated.
* tests: add excess_length_strategy tests
* doc: updated return value description for drop_long_seq_in_dataset
* add @enable_hf_offline
* fixed cfg modified after validate_config called
* hf offline fix
* fix tqdm desc when raise is used
* test: added test for non-batched case
* accidental code change revert
* test: use pytest.raises
* test: simplified drop_seq_len tests
* test: moved excess_length_strat test to test_data.py
---------
Co-authored-by: salman <salman.mohammadi@outlook.com>
* METRIC_PRECISION-> 8
* use ndigits and move env getter to top of log function
---------
Co-authored-by: Ved <ved.work2024@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
