# EBFT Strided: LoRA Llama-3.2-3B on SwallowCode, 100 steps
# Actor on GPU 0, frozen feature network on GPU 1
#
# Run: CUDA_VISIBLE_DEVICES=0,1 python -m axolotl.cli.train examples/ebft/llama-3b-ebft-strided-fft.yaml

base_model: meta-llama/Llama-3.2-3B
rl: ebft

ebft:
  mode: strided
  stride: 8
  context_length: 8
  generate_max_len: 8
  n_samples_per_prompt: 4
  temperature: 0.6
  top_p: 1.0
  feature_layers: [0.25, 0.5, 0.75]
  embed_method: last_token
  use_whitening: true
  alignment_coef: 1.0
  diversity_coef: 1.0
  rl_coef: 1.0
  ce_coef: 0.0   # paper recommends 0.03 for mixed objective; 0.1 causes CE to dominate
  advantage_estimator: rloo

datasets:
  - path: sjelassi/swallow_code_20m
    type: ebft_pretrain.transform
    split: train[:5000]

sequence_len: 1024
micro_batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 1
max_steps: 100

learning_rate: 1.0e-5
optimizer: adamw_torch_fused
lr_scheduler: cosine
warmup_steps: 10
weight_decay: 0.01

adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

bf16: auto
torch_dtype: bfloat16
flash_attention: false
gradient_checkpointing: true
torch_compile: true
gradient_checkpointing_kwargs:
  use_reentrant: true
ddp: false
device_map:
  "": 0

special_tokens:
  pad_token: "<|end_of_text|>"

val_set_size: 0.0
output_dir: ./outputs/ebft-llama3b-strided

wandb_project: ebft
wandb_name: llama3b-strided-lora-100steps
logging_steps: 1
save_steps: 50