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instella-3b-sft.yaml
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117 lines (100 loc) · 2.47 KB
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run_name: instella-3b-sft
seed: 6198
dry_run: false
wandb:
name: ${run_name}
project: instella
group: sft
model:
d_model: 2560
n_heads: 32
n_layers: 36
mlp_hidden_size: 13824
weight_tying: false
alibi: false
rope: true
flash_attention: true
attention_dropout: 0.0
attention_layer_norm: true
multi_query_attention: false
include_bias: false
block_type: sequential
layer_norm_type: rms
layer_norm_with_affine: true
bias_for_layer_norm: false
attention_layer_norm_with_affine: true
activation_type: swiglu
residual_dropout: 0.0
embedding_dropout: 0.0
max_sequence_length: 4096
vocab_size: 50280
embedding_size: 50304
eos_token_id: 0
pad_token_id: 1
init_device: meta
init_fn: mitchell
compile:
fullgraph: false
optimizer:
name: adamw
learning_rate: 1.0e-5
weight_decay: 0.1
decay_norm_and_bias: true
decay_embeddings: true
eps: 1e-8
betas:
- 0.9
- 0.95
metrics_log_interval: 10
scheduler:
name: linear_with_warmup
t_warmup: 500
alpha_f: 0.001
tokenizer:
identifier: tokenizers/allenai_eleuther-ai-gpt-neox-20b-pii-special.json
truncate_direction: right
save_folder: outputs/sft/${run_name}
save_overwrite: true
# Sharded checkpoints (best for restarts)
save_interval: 1000
save_num_checkpoints_to_keep: 9
# Unsharded checkpoints (for final storage)
save_interval_unsharded: 10000
save_num_unsharded_checkpoints_to_keep: -1
load_path: outputs/pretrain/instella-3b-pretrain/step13727-unsharded
reset_trainer_state: true
reset_optimizer_state: true
max_duration: 3ep
global_train_batch_size: 128 #
device_train_microbatch_size: 4
precision: amp_bf16
fsdp:
wrapping_strategy: null
precision: mixed
sharding_strategy: FULL_SHARD
max_grad_norm: 1.0
max_grad_norm_ratio: null
speed_monitor:
window_size: 20
normalize_by_label_mask: true
data:
pad_direction: right
num_workers: 32
drop_last: true
pin_memory: true
prefetch_factor: 1
persistent_workers: true
timeout: 0
generate_attention_mask: false
paths:
- datasets/sft/smoltalk/input_ids.npy
- datasets/sft/openmathinstruct2_1M/input_ids.npy
- datasets/sft/tulu3-if/input_ids.npy
- datasets/sft/o1-journey-10x/input_ids.npy
- datasets/sft/mmlu/input_ids.npy
label_mask_paths:
- datasets/sft/smoltalk/label_mask.npy
- datasets/sft/openmathinstruct2_1M/label_mask.npy
- datasets/sft/tulu3-if/label_mask.npy
- datasets/sft/o1-journey-10x/label_mask.npy
- datasets/sft/mmlu/label_mask.npy