69 lines
1.4 KiB
YAML
69 lines
1.4 KiB
YAML
# EBFT Strided: LoRA Llama-3.2-3B on SwallowCode, 100 steps
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# Actor on GPU 0, frozen feature network on GPU 1
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#
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# Run: CUDA_VISIBLE_DEVICES=0,1 python -m axolotl.cli.train examples/ebft/llama-3b-ebft-strided-fft.yaml
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base_model: meta-llama/Llama-3.2-3B
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rl: ebft
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ebft:
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mode: strided
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stride: 8
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context_length: 8
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generate_max_len: 8
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n_samples_per_prompt: 4
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temperature: 0.6
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top_p: 1.0
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feature_layers: [0.25, 0.5, 0.75]
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embed_method: last_token
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use_whitening: true
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alignment_coef: 1.0
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diversity_coef: 1.0
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rl_coef: 1.0
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ce_coef: 0.0 # paper recommends 0.03 for mixed objective; 0.1 causes CE to dominate
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advantage_estimator: rloo
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datasets:
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- path: sjelassi/swallow_code_20m
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type: ebft_pretrain.transform
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split: train[:5000]
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sequence_len: 1024
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micro_batch_size: 1
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gradient_accumulation_steps: 4
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num_epochs: 1
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max_steps: 100
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learning_rate: 1.0e-5
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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warmup_steps: 10
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weight_decay: 0.01
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adapter: lora
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_linear: true
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bf16: auto
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torch_dtype: bfloat16
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gradient_checkpointing: true
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torch_compile: true
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gradient_checkpointing_kwargs:
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use_reentrant: true
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ddp: false
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device_map:
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"": 0
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special_tokens:
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pad_token: "<|end_of_text|>"
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val_set_size: 0.0
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output_dir: ./outputs/ebft-llama3b-strided
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wandb_project: ebft
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wandb_name: llama3b-strided-lora-100steps
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logging_steps: 1
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save_steps: 50
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