65 lines
1.8 KiB
YAML
65 lines
1.8 KiB
YAML
# EBFT Strided Structured Mode: For structured (prompt, completion) data
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# Uses strided block-parallel generation on completion spans — no vLLM needed.
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#
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# Run: CUDA_VISIBLE_DEVICES=0 axolotl train examples/ebft/llama-1b-ebft-strided-structured.yaml
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base_model: meta-llama/Llama-3.2-1B
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rl: ebft
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ebft:
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mode: strided # strided block-parallel generation
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stride: 8 # tokens between anchor points
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context_length: 8 # context window per block
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generate_max_len: 8 # tokens to generate per block
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n_samples_per_prompt: 4 # rollouts per document
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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: 0.03 # small CE weight for structured data
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advantage_estimator: rloo
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min_completion_prefix: 8 # skip anchors too close to prompt boundary
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datasets:
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- path: nvidia/OpenCodeInstruct
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type: ebft_strided_structured.transform
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split: train[:1%]
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sequence_len: 2048
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micro_batch_size: 1
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gradient_accumulation_steps: 2
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num_epochs: 1
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# max_steps: 10
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learning_rate: 1.0e-6
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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warmup_steps: 5
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adapter: lora
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lora_r: 16
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lora_alpha: 32
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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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attn_implementation: flex_attention
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# (full-model compile conflicts with gradient checkpointing + flex_attention)
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: true # required for flex_attention (non-reentrant causes CheckpointError)
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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-strided-structured
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wandb_project: ebft
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logging_steps: 1
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save_steps: 100
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