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Upload cfg.yaml
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architecture:
backbone_dtype: float16
force_embedding_gradients: false
gradient_checkpointing: true
intermediate_dropout: 0.0
pretrained: true
pretrained_weights: ''
augmentation:
random_parent_probability: 0.1
skip_parent_probability: 0.0
token_mask_probability: 0.0
dataset:
add_eos_token_to_answer: true
add_eos_token_to_prompt: true
answer_column: output
data_sample: 1.0
data_sample_choice:
- Train
- Validation
mask_prompt_labels: false
parent_id_column: parent_id
prompt_column:
- instruction
text_answer_separator: <|answer|>
text_prompt_start: <|prompt|>
train_dataframe: data/user/oasst/train_full_multilang_allrank.pq
validation_dataframe: data/user/oasst/gpt4_val_v0.csv
validation_size: 0.01
validation_strategy: custom
environment:
compile_model: false
find_unused_parameters: false
gpus:
- '0'
- '1'
- '2'
- '3'
mixed_precision: true
number_of_workers: 8
seed: -1
trust_remote_code: false
use_fsdp: false
experiment_name: h2ogpt-gm-oasst1-multilang-1024-20b
llm_backbone: EleutherAI/gpt-neox-20b
logging:
logger: Neptune
neptune_project: Zoo/h2o-llm
number_of_texts: 10
output_directory: output/user/h2ogpt-gm-oasst1-multilang-1024-20b/
prediction:
batch_size_inference: 1
do_sample: false
max_length_inference: 256
metric: GPT3.5
min_length_inference: 2
num_beams: 2
repetition_penalty: 1.2
stop_tokens: ''
temperature: 0.3
problem_type: text_causal_language_modeling
tokenizer:
add_prefix_space: false
add_prompt_answer_tokens: false
max_length: 1024
max_length_answer: 512
max_length_prompt: 512
padding_quantile: 1.0
training:
batch_size: 8
differential_learning_rate: 1.0e-05
differential_learning_rate_layers: []
drop_last_batch: true
epochs: 2
evaluate_before_training: true
evaluation_epochs: 0.5
grad_accumulation: 1
gradient_clip: 0.0
learning_rate: 0.0005
lora: true
lora_alpha: 33
lora_dropout: 0.05
lora_r: 16
lora_target_modules: ''
loss_function: CrossEntropy
optimizer: AdamW
save_best_checkpoint: false
schedule: Cosine
train_validation_data: false
warmup_epochs: 0.0
weight_decay: 0.0