* 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
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Co-authored-by: NanoCode012 <nano@axolotl.ai>
