# FP8 training example with FSDP
# This config demonstrates FP8 mixed precision training using HuggingFace Accelerate
# with FSDP for distributed training and float8 all-gather optimization

### Model configuration
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
trust_remote_code: true

### Method configuration
stage: sft
do_train: true
finetuning_type: full

### Dataset configuration
dataset: identity
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16

### Output configuration
output_dir: saves/llama3-8b/fp8-fsdp/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true

### Training configuration
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 5.0e-5
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true

### FP8 configuration
fp8: true
fp8_backend: torchao  # Use TorchAO backend for FP8
fp8_enable_fsdp_float8_all_gather: true  # Enable FSDP2 float8 all-gather optimization

### FSDP configuration (using training arguments - no separate FSDP config file)
fsdp:
  - full_shard
  - auto_wrap
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer

### Logging configuration
report_to: wandb
run_name: llama3_fp8_fsdp_sft