* feat: add sageattention
* feat: call path on pre model load
* fix: patch to use register to correct var
* fix: add strict check import at start
* chore: fix comments
* chore: refactor
* feat: add capability check
* fix: missed underscore
* fix: let sageattention use FA backend in transformers
* feat: update sage attention for attention mask and position ids
* feat: allow sample packing but add warning without packing
* fix: loss hitting 0 with packing and attention mask note
* feat: downcast embeds if sage attention too
* feat: add config validation
* feat: add attention docs
* chore: docs
* 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>
* 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>
* When training of function calls, "tools" elements of a dataset can contain same parameter name but with different types. Datasets fails to load such training set. This fix allows "parameters" element of function call to be string( by running "json.dumps" in preparation of training data set). The _get_tools function will iterate over tool definitions, if "parameters" element is dict, it will keep that way, if it is a string, it will be converted to dict by invoking "json.loads" on string value.
* feat: add doc on tool parameters json loading
* feat: add tests for parameters json string
---------
Co-authored-by: ezlotnik <eduard_zlotnik@intuit.com>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* fix: force train split for json,csv,txt for test_datasets
* feat(doc): add info on mixing datasets for VLM
* feat(doc): max memory
* fix(doc): clarify lr groups
* fix: add info on vision not being dropped
* feat: add qwen3-vl to multimodal docs
* fix: add moe blocks to arch list
* feat(doc): improve mistral docs
* chore: add helpful link [skip-e2e]
* fix: add vram usage for mistral small
* Update link in docs/faq.qmd
Co-authored-by: salman <salman.mohammadi@outlook.com>
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
* Add chat_template.argilla_chat support for DPO datasets
Creates a new chat_template.argilla_chat prompt strategy for handling
DPO datasets where chosen/rejected fields contain full conversations
(messages + final response), following the pattern of chatml.argilla_chat
and llama3.argilla_chat.
- Add argilla_chat() function to chat_template.py
- Add chat_template.argilla_chat to RLHF documentation
- Add test coverage for argilla_chat with multiple tokenizers
Dataset format:
{
"chosen": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
],
"rejected": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]
}
* Fix chat_template.argilla_chat return value contract and add docstring
- Return (transform_fn, dataset_kwargs) tuple instead of bare transform_fn
- Add remove_columns specification for field_chosen and field_rejected
- Add comprehensive docstring with Args/Returns sections
- Update tests to unpack tuple return value
Addresses PR feedback to maintain consistency with chat_template.default()
and properly specify columns to remove after dataset transformation.
* Update tests/prompt_strategies/test_dpo_chat_templates.py
Co-authored-by: Wing Lian <wing.lian@gmail.com>
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* feat: add center_rewards_coefficient for reward modeling
- Add center_rewards_coefficient parameter to Pydantic schema with paper reference
- Pass parameter through base builder and causal builder to training args
- Add documentation section with usage examples and theoretical background
- Enable parameter in reward modeling example configs with recommended value
- Enables reward centering for improved training stability in RLHF workflows
Implements auxiliary loss from Eisenstein et al. 2023 (https://huggingface.co/papers/2312.09244)
to incentivize mean-zero reward outputs without post-training normalization.
* Update description
* test: add unit tests for center_rewards_coefficient integration
* Update src/axolotl/core/builders/base.py
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* Update docs/reward_modelling.qmd
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* Update docs/reward_modelling.qmd
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* reference to TRL documentation.
* add new reward model configuration for qwen3 with comprehensive parameters
* Verified center_rewards_coefficient is correctly passed through the trainer builder to training arguments.
* Refactor reward modeling documentation to consolidate information on center_rewards_coefficient
* Remove unit tests for center_rewards_coefficient integration as part of codebase cleanup.
* linting
* nit
* Apply suggestions from code review
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* lint
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* fix to not use batch feature indexing
* more vlm fixes
* use AutoModelForImageTextToText
* add example yaml and need num2words for chat template
* improve handling of adding image tokens to conversation
* add lfm2-vl support
* update the lfm readme
* fix markdown and add rtol for loss checks
* feat: add smolvlm2 processing strat
* fix: check for causal-conv1d in lfm models
* feat: add docs for lfm2
* feat: add new models and tips to docs
* feat: add smolvlm2 docs and remove extra dep
* chore: update docs
* feat: add video instructions
* chore: cleanup
* chore: comments
* fix: typo
* feat: add usage stats
* chore: refactor
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* Add support for Dion optimizer
* dion training kwargs
* fix var names
* no dion 8bit for now
* use updated axolotl-contribs-mit for dion optimizer
* add smoke test for dion optimizer
* add docs
* fix typo during edits
* fix test to not remove load in 8bit
* feat(doc): add vastai link
* feat: add cloud providers to readme for more visibility
* add prime intellect, remove Modal as sponsor
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* feat: add gemma3n cce
* feat: add sample config
* feat: add gemma3n multimodal mode
* feat: add audio example
* feat: support audio and return pixel values in collator
* feat: support unmask only assistant region (gemma3n for now)
* feat(doc): add notes for audio loading
* feat: add audio support for gemma3n
* feat: update examples
* feat: add gemma3n to the docs
* fix: add link at top
* feat(doc): clarify additional requirements
* fix: mllama missing aspect ratio
* fix: mllama need attention fixes for fa2
* Partially Revert "fix: mllama need attention fixes for fa2"
This reverts commit a0bfdd1777.
* fix: disable FA2 for mllama in vision mode
* feat: update configs to use proper attention
* fix: support other vision features
* feat(doc): clarify requirements for gemma3n
* use cuda streams for activation offloading
* use torch native ops
* update cfg schema for streams
* fix literal constructor for set
* use context for training step so it doesn't affect evals
* disable streams
* auto gc on eval steps
* use activation_offloading config arg
* add docs for gradient checkpointing
* handle validation for gc/ao
* use cuda streams for act offloading
* add more validation for AC w/o GC
* fix docs
* move activation_offloading lower in definition so it doesn't break args/kwargs
* fix kd due to import order
