TDPOM-beta / cfg.yaml
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architecture:
backbone_dtype: int4
gradient_checkpointing: true
intermediate_dropout: 0.0
pretrained: true
pretrained_weights: ''
augmentation:
neftune_noise_alpha: 0.0
random_parent_probability: 0.0
skip_parent_probability: 0.0
token_mask_probability: 0.0
dataset:
add_eos_token_to_answer: true
add_eos_token_to_prompt: true
add_eos_token_to_system: true
answer_column: chosen
chatbot_author: H2O.ai
chatbot_name: h2oGPT
data_sample: 1.0
data_sample_choice:
- Train
- Validation
limit_chained_samples: true
mask_prompt_labels: true
only_last_answer: false
parent_id_column: None
personalize: false
prompt_column:
- question
prompt_column_separator: \n\n
rejected_answer_column: rejected
rejected_prompt_column: None
system_column: system
text_answer_separator: <|answer|>
text_prompt_start: <|prompt|>
text_system_start: <|system|>
train_dataframe: /train.pq
validation_dataframe: None
validation_size: 0.01
validation_strategy: automatic
environment:
compile_model: false
deepspeed_allgather_bucket_size: 1000000
deepspeed_method: ZeRO2
deepspeed_reduce_bucket_size: 1000000
deepspeed_stage3_param_persistence_threshold: 1000000
deepspeed_stage3_prefetch_bucket_size: 1000000
find_unused_parameters: false
gpus:
- '0'
huggingface_branch: main
mixed_precision: true
mixed_precision_dtype: bfloat16
number_of_workers: 8
seed: -1
trust_remote_code: true
use_deepspeed: false
experiment_name: TDPOM-beta
llm_backbone: h2oai/h2o-danube3-500m-chat
logging:
log_all_ranks: false
log_step_size: absolute
logger: None
neptune_project: ''
wandb_entity: ''
wandb_project: ''
output_directory: /output/TDPOM-beta/
prediction:
batch_size_inference: 0
do_sample: false
max_length_inference: 256
max_time: 0.0
metric: BLEU
metric_gpt_model: gpt-3.5-turbo-0301
metric_gpt_template: general
min_length_inference: 2
num_beams: 1
num_history: 4
repetition_penalty: 1.0
stop_tokens: ''
temperature: 0.0
top_k: 0
top_p: 1.0
problem_type: text_dpo_modeling
tokenizer:
add_prompt_answer_tokens: false
max_length: 512
padding_quantile: 1.0
tokenizer_kwargs: '{"use_fast": true, "add_prefix_space": false}'
training:
attention_implementation: auto
batch_size: 2
beta: 0.05
differential_learning_rate: 1.0e-05
differential_learning_rate_layers: []
drop_last_batch: true
epochs: 1
evaluate_before_training: false
evaluation_epochs: 1.0
freeze_layers: []
grad_accumulation: 1
gradient_clip: 10.0
learning_rate: 0.0001
lora: true
lora_alpha: 16
lora_dropout: 0.05
lora_r: 4
lora_target_modules: ''
lora_unfreeze_layers: []
loss_function: DPOLoss
min_learning_rate_ratio: 0.0
optimizer: AdamW
save_checkpoint: last
schedule: Cosine
simpo_gamma: 1.0
train_validation_data: false
use_dora: false
use_rslora: false
warmup_epochs: 0.0
weight_decay: 0.0