54dd7abfc1
* adding model_cfg to set num_labels * using a num_labels field instead * linting * WIP stepwise prompt tokenizer * this should work? * trainer working? * pushing to runpod * fixing saving * updating conf * updating config, adding docs * adding stepwise supervision docpage * updating tests * adding test for dataset * fixing tests * linting * addressing some comments * adding additional cfg fields support * updating tests, fixing cfg * fixing tests * updating loss * Update test_process_reward_model_smollm2.py * updating loss values and seed * dumb pre-commit
68 lines
1.3 KiB
YAML
68 lines
1.3 KiB
YAML
base_model: Qwen/Qwen2.5-0.5B
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# optionally might have model_type or tokenizer_type
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model_type: AutoModelForSequenceClassification
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num_labels: 1
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tokenizer_type: AutoTokenizer
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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reward_model: true
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chat_template: qwen_25
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datasets:
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- path: argilla/distilabel-intel-orca-dpo-pairs
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type: bradley_terry.chat_template
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val_set_size: 0.0
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output_dir: ./outputs/out
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remove_unused_columns: false
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sequence_len: 2048
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sample_packing: false
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eval_sample_packing: false
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pad_to_sequence_len: true
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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: true
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fp16:
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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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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warmup_ratio: 0.1
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evals_per_epoch:
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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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