* 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
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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
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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
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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
* FSDP2 args migration implementation
This commit implements the migration to FSDP2 arguments including:
- FSDP2 support with LoRA training
- DPO integration with FSDP2
- Model loading fixes and refactoring
- CPU offloading and PEFT handling
- Test updates and CI improvements
- Bug fixes for dtype errors and various edge cases
* tiled_mlp supports single gpu
* use checkpoint offloading for arctic training
* patch torch checkpoint too
* support for single gpu zero3
* add linkback to where it was copied from
* densemixer plugin integration
* update readme with usage docs
* automatically find new integrations that aren't explicitly defined
* make sure to import os
* fix: let users know to not call preprocess for vision mode
* fix: improve ux for pretraining dataset and skip prepare ds
* feat: add info to doc
* Update src/axolotl/cli/preprocess.py following comment
Co-authored-by: salman <salman.mohammadi@outlook.com>
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Co-authored-by: salman <salman.mohammadi@outlook.com>
* build base images for torch 2.7.1
* fix: update base docker to use torch 2.7.1
* fix: update doc for main base to use 2.7.1
* make sure to install fa2 in base uv too
* use no build isolation for uv+flashattn
* install psutil also for fa2
* longer timeout for flash attn build
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* bump hf deps
* upgrade liger-kernel too
* install cce from fork for transformers fix
* fix reference to vocab size in gemma3 patch
* use padding_idx instead of pad_token_id
* remove fixed gemma3 patch
* use updated cce fork
* fix local mllama cce patches w docstring
* add test for multipack with trainer setup and fix trainer for trainer refactor upstream
* bump modal version
* guard for iterable datasetS
* mllama model arch layout changed in latest transformers
* fix batch sampler with drop_last
* fix: address upstream vlm changes for lora
* fix: update references to old lora target path
* fix: remove mllama fa2 patch due to upstream fix
* fix: lora kernel patch path for multimodal models
* fix: removed mllama from quarto
* run test for came optim on 2.6.0+
* fix fsdp2 patch and remove deprecated patch
* make sure to set sequence_parallel_degree for grpo
* Add SP test for GRPO
* add sp to grpo config for trainer
* use reward_funcs as kwarg to grpo trainer
* fix the comprehension for reward funcs
* reward funcs already passed in as args
* init sp_group right before training
* fix check for adding models to SP context
* make sure to pass args to super
* upgrade deepspeed
* use updated trl and add reasoning flags for vllm
* patch the worker
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* feat: add num_proc and load from cache for rl mapping
* fix: refactor sft and rl trainer to set same base args
* feat: add report_to to set run name
* fix: consolidate handling of fp16, bf16, tf32 kwarg
* chore: consolidate eval_strat, loraplus, lr sched, max_length
* fix: deprecate old types
* fix: adding missing Any
* fix: max_steps incorrectly set
* fix: remove unnecessary datacollator kwarg insert and pop
* fix: update default max_steps
* fix: add missing weight_decay handling
* fix: ignore max_length for grpo
* feat: update CI on trainer_builder
* fix: comments
* improve handling of warmup/logging steps
* use transformers default for logging steps, not None
* fix: remove redundant override
* fix: lint
* feat: allow custom optim for rl methods
* fix: duplicate optim setting
* fix(test): set sequence_parallel_degree default in base cfg
* feat: add handling for seed and SP/ring-attn config
* chore: add back return typing from rebase
* fix(test): use RLType directly to skip needing to validate
* feat: split training builder into sub modules
* fix: remove deprecated clause
* chore: add missing config to doc
* fix: update quarto autodoc
* fix: import path for trainer builder and submodules
* fix: remove redundant configs from rebase mistake
* chore: simplify dynamo check
* fix: optimizer_cls_and_kwargs to be passed into trainer_kwargs
* fix: add missing rex from rebase
* fix: move pop optimizer_cls_and_kwargs
* fix: pop optimizer cls in rl too
* fix: leftover bug from rebase
* fix: update handling of trainer_cls in RL
* fix: address pr feedback
* feat: call hook_pre_create_trainer for rl
* chore: lint
* fix: return notimplemented for ppo
* feat: moved torch compile to base and refactor collator setting
* chore: remove unused importlib.util import
* fix: optimizer cls not being popped
* feat: move epoch setting to base
* fix: catch unhandled custom optimizer
* fix: remove duplicate lora plus setting
* chore: refactor if condition
* chore: refactor set_base_training_args into smaller modules
* fix: address TrainerBuilderBase class variables to instance var
* fix: add handling for beta3 and episilon2
* fix: change to pass dict via arg instead of updating dict
* chore: simplify if condition
* fix: force access to lr & weight decay in case not provided to early error
* fix: remove log sweep
* chore: refactor if condition
* fix: address renamed cfg
* fix: improve handling of cosine hyp
* fix: remove unused params
* chore: refactor
* chore: clarify doc safetensors
* fix: update import path to be unified following comments
* fix: duplicate kwargs passed
* feat: return separate trainer_kwargs
* chore: refactor
* chore: refactor based on comments
* chore: refactor based on comments
* fix: move gpustats callback to base
* chore: create trainer_cls_args first based on comments
* fix: ipo label smoothing passed incorrectly
* feat: add optimizer parity for RL methods with test
* feat: add parity for optimizer in RM/PRM and add test
* fix: remove redundant function override for orpo/cpo batch metrics
* fix: improve handling of dpo_label_smoothing and merge issue
* fix: test fixture returning wrong field
* fix: address avoid direct modify fixture
* chore: minor refactor
* Revert "chore: refactor"
This reverts commit 99c8859eb0.
* feat: rename trainer_builder to builders
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Co-authored-by: Wing Lian <wing@axolotl.ai>
