c50c4acbf4
* EBFT wip * fixes * more fixeS * add missing strided module * ebft fixes for multi-turn * make ebft work with async * add example for ebft w qwen3.5 * fix for split thinking and update yaml for lora over linear attention only * enforce_eager for vllm arg in schema * fix sync weights * fix multi-gpu * handle updated sig for mm * ddp fixes * improve multi-gpu handling, don't calculate logits, adaptive completion length * chore: lint * chore: lint * support completion_mean * Address corereview feedback * clamp min IS ratio * Address PR code review * more fixes identified * address code review * Fix property from rebase conflict
82 lines
2.2 KiB
YAML
82 lines
2.2 KiB
YAML
# EBFT: Energy-Based Fine-Tuning with Llama-3.2-1B on OpenCodeInstruct
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#
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# Paper: "Matching Features, Not Tokens" (Jelassi et al., 2026)
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# https://arxiv.org/abs/2603.12248
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#
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# Prerequisites:
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# 1. Start vLLM server on a separate GPU:
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# CUDA_VISIBLE_DEVICES=1 python -m trl.scripts.vllm_serve \
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# --model meta-llama/Llama-3.2-1B \
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# --host 0.0.0.0 --port 8000 \
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# --gpu-memory-utilization 0.4 --dtype bfloat16
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#
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# 2. Run training:
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# CUDA_VISIBLE_DEVICES=0 axolotl train examples/ebft/llama-1b-ebft-opencode.yaml
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base_model: meta-llama/Llama-3.2-1B
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chat_template: llama3
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# --- Training method ---
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rl: ebft
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# --- EBFT configuration ---
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ebft:
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feature_layers: [0.25, 0.5, 0.75] # extract hidden states at 25%, 50%, 75% depth
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embed_method: last_token # pool to sequence embedding via last token
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use_whitening: false # SVD whitening (disable for speed in small runs)
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alignment_coef: 1.0 # cosine similarity with ground-truth features
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diversity_coef: 1.0 # pairwise similarity penalty
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ce_coef: 0.0 # cross-entropy on ground-truth (0 = pure feature matching)
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# --- Generation settings (via TRL/GRPO infrastructure) ---
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trl:
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num_generations: 4 # samples per prompt for RLOO
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max_completion_length: 256 # max generated tokens
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temperature: 1.0
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use_vllm: true
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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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# --- Dataset ---
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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%] # first 1% for validation runs
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# --- Training hyperparameters ---
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sequence_len: 1024
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micro_batch_size: 2
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gradient_accumulation_steps: 4
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num_epochs: 1
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max_steps: 50
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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: 5
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weight_decay: 0.01
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# --- LoRA (recommended to reduce memory with frozen feature network) ---
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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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# --- Hardware ---
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bf16: auto
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flash_attention: true
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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-llama-1b-opencode
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# --- Logging ---
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use_tensorboard: true
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logging_steps: 1
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save_steps: 25
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