65 lines
1.2 KiB
YAML
65 lines
1.2 KiB
YAML
# EBFT validation config — no vLLM, uses HF generate for simplicity
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# Run: CUDA_VISIBLE_DEVICES=0 axolotl train examples/ebft/llama-1b-ebft-opencode-novllm.yaml
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base_model: meta-llama/Llama-3.2-1B
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chat_template: llama3
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rl: ebft
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ebft:
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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: false
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alignment_coef: 1.0
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diversity_coef: 1.0
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ce_coef: 0.0
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trl:
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num_generations: 4
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max_completion_length: 128
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temperature: 1.0
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use_vllm: false
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scale_rewards: true
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loss_type: grpo
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epsilon: 0.2
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datasets:
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- path: nvidia/OpenCodeInstruct
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type: ebft_opencode.transform
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split: train[:1%]
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sequence_len: 512
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micro_batch_size: 2
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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-5
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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warmup_steps: 2
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weight_decay: 0.01
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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: flash_attention_2
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gradient_checkpointing: true
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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-validation
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wandb_project: ebft
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wandb_run_id:
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wandb_watch:
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wandb_log_model:
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logging_steps: 1
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save_steps: 100
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