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axolotl/examples/alst/llama3-8b-fsdp2-alst.yaml

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YAML

base_model: meta-llama/Llama-3.1-8B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
datasets:
- path: togethercomputer/Long-Data-Collections
type: completion
field: text
data_files:
- pretrain/rp_sub.jsonl.zst
- path: princeton-nlp/TextbookChapters
type: completion
field: chapter
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out
sequence_len: 500_000
min_sample_len: 200_000
sample_packing: true
tiled_mlp: true
context_parallel_size: 8
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 2e-5
bf16: auto
tf32: true
gradient_checkpointing: true
activation_offloading: legacy
resume_from_checkpoint:
logging_steps: 1
attn_implementation: flash_attention_2
warmup_steps: 100
saves_per_epoch: 1
evals_per_epoch: 2
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
fsdp_version: 2
fsdp_config:
offload_params: false # offloading is currently not compatible with SP + torchao optimizer
state_dict_type: SHARDED_STATE_DICT
auto_wrap_policy: TRANSFORMER_BASED_WRAP
transformer_layer_cls_to_wrap: LlamaDecoderLayer
reshard_after_forward: true
# save_first_step: true # uncomment this to validate checkpoint saving works with your config