b620ed94d0
* Add example YAML file for training Mistral using DPO * added deduplication code * Add exact deduplication feature and update examples * Improve deduplication for train/eval overlap Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements. * Improve deduplication for train/eval overlap Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements. * Apply suggestions from code review To handle the original case where we do not do deduplication Co-authored-by: Wing Lian <wing.lian@gmail.com> * Improve false collision detection to ensure dataset integrity - Added test cases to simulate and verify handling of forced hash collisions between datasets. - Ensured that datasets with identical hashes but different content are correctly identified, preventing incorrect deduplication. - Updated unit tests to include scenarios where collisions occur across both training and evaluation datasets, as well as within a single dataset. * Moved the constants file to the tests folder - Relocated `constants.py` to the `tests` folder to improve modularity and maintain a clear separation between source and test files. - Renamed `cicd/tests.py` to `cicd/cicd_tests.py` to resolve a conflict with `tests/__init__.py`, which caused Mypy to fail due to duplicate module names. - Updated all references to `cicd.tests` in the codebase to `cicd.cicd_tests` to reflect the renaming and ensure compatibility. - These changes ensure Mypy passes the pre-commit hook and maintain alignment with the project's structure. * revert some changes from previous commit and fix relative import --------- Co-authored-by: Wing Lian <wing.lian@gmail.com> Co-authored-by: Wing Lian <wing@axolotl.ai>
77 lines
1.3 KiB
YAML
77 lines
1.3 KiB
YAML
base_model: meta-llama/Llama-3.2-1B
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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datasets:
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- path: mhenrichsen/alpaca_2k_test
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type: alpaca
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- path: mhenrichsen/alpaca_2k_test
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type: alpaca
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dataset_prepared_path:
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val_set_size: 0.0
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output_dir: ./outputs/lora-out
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dataset_exact_deduplication: true
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test_value: true
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sequence_len: 4096
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sample_packing: true
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eval_sample_packing: false
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pad_to_sequence_len: true
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adapter: lora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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lora_modules_to_save:
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- embed_tokens
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- lm_head
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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s2_attention:
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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <|end_of_text|>
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