* 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>
* 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>
* 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>
* 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>
* fix: transformers deprecate load_in_Xbit in model_kwargs
* fix: test to read from quantization_config kwarg
* fix: test
* fix: access
* fix: test weirdly entering incorrect config
- Fix _loss_function attribute not found on base model with PEFT
- Fix mismatched attribute name (loss_function vs _loss_function)
- Set _loss_function on unwrapped base model for PEFT
- Enable previously skipped test_llama_lora_kd test
- Add test config fixes for LoRA kernel compatibility
Fixes https://github.com/axolotl-ai-cloud/axolotl/issues/3206
* make sure to use ray prepare for dataloader fixes
* ray tests use 2.7.0+
* don't call init_distributed w ray and deepspeed
* handle dict deepspeed config
* better handling of dict deepspeed config
* use json.dumps
* guard to_dict
* wrap import for optional ray
* upgrade transformers to 4.57.0
* remove deprecated autoawq and use latest peft
* remove autoawq from setuptools script
* fix imports
* make sure torchvision is installed
* remove support for BetterTransformer
* skip fsdp_qlora_prequant test
* more robust error reporting
* feat: upgrade transformers to v4.56
* fix handling of CP/SP now that position_ids are default even for unpacked sequences
* feat: monkeypatch list_repo_templates
* fix: apply patch for tests only
* see if updated main works at least
* fix: update to patch release and remove monkeypatch
* remove fsdp2 eval patch
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* improve fsdp shard merging
* improve logging
* update information on merging and inferencing GPT-OSS
* cleanup readme
* automate cleanup of FSDP prefix
* import GRPO only if necessary
* only modify config.json on rank0
* merge final checkpoint at end of training
* prevent circular import
* Fix saving for sharded state dict
* devx, move merged to output dir
* move import back to top
* Fix stuck merge
* fix conditionals from pr feedback and add test
* 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>
* use exec instead of subprocess to make ctrl+c nicer for cli
* change var name to use_exec
* simplify to bool
* flush std*
* patch subprocess as mock in test
* fix tests
* more test fixes
* use nanmena for loss aggregation (CP fix)
* use regular asserts
* small changes to make tests isolate
* combining evaluation_loop patches
* fix
* delete unused
* fix check
* fix for parallelism config from trainer
* fix handling of parallelism_config w accelerate
* add todo for removal
* update to latest axolotl-contribs-mit for optimizer fix too
* synchronize training after checkpoint save
* dir spelling
* use latest accelerate main
* fix to not use partial state parallelism_config
* more fixeS
* use most recent accelerate fix
* fix cpu_ram_efficient_loading to meta devices from rank 0 to prevent CPU RAM oom
* improve handling of broadcasting fsdp2 state dict
* support for openai chat template with thinking key as the reasoning trace
* address PR feedback
* refactor to remove dependency on PartialState for parallelism config
* bump accelerate, gptoss fixes
* limit meta fixes to fsdp2 for now
* fixes for gpt oss
* fixup examples, don't use cpu-ram-efficient-loading for now
* remove problematic barrier
* patch parallelism config
* reorder comparison
* device mesh fixes
* make pure CP work
* lint
