88 lines
3.4 KiB
YAML
88 lines
3.4 KiB
YAML
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base_model: meta-llama/Llama-3.1-8B-Instruct
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: false
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load_in_4bit: false
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# --- Dataset: continued pre-training with a masked constant prefix ---
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# type: input_output gives per-segment loss masking (template-free). The dataset
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# stores `segments`: the prefix "<|begin_of_text|>Game of bjk\n\n" is label:false
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# (attended to as context, but NOT in the loss); the space-separated moves + trailing
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# <|end_of_text|> are label:true (trained), like a Llama-3 pre-training document.
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# Built by data/push_input_output_dataset.py.
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# Trains on ALL four cumulative shards (full 50k-game scale). Listing multiple
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# dataset entries is the reliable way to combine them — Axolotl concatenates them.
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# To train at a smaller scale, delete trailing entries (shards are cumulative):
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# 25k -> keep first three; 10k -> keep first two; 5k -> keep only the first.
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# (Concise alternative, if your Axolotl forwards split arithmetic to load_dataset:
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# a single entry with split: games_0_5k+games_5k_10k+games_10k_25k+games_25k_50k)
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datasets:
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- path: cfierro/othello-snake-llama3-fixed-prefix
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type: input_output
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split: games_0_5k
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- path: cfierro/othello-snake-llama3-fixed-prefix
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type: input_output
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split: games_5k_10k
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- path: cfierro/othello-snake-llama3-fixed-prefix
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type: input_output
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split: games_10k_25k
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- path: cfierro/othello-snake-llama3-fixed-prefix
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type: input_output
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split: games_25k_50k
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train_on_inputs: false # REQUIRED for input_output masking to take effect
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dataset_prepared_path: /scratch/project/eu-26-55/knowledge-ft/axolotl/datasets/llama-3.1-8b/othello-snake-llama3-fixed-prefix
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val_set_size: 0.02
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output_dir: /scratch/project/eu-26-55/knowledge-ft/axolotl/models/llama-3.1-8b-fft-othello-snake-fixed-prefix-2e-5
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sequence_len: 1024 # games are ~200 tokens max; long context is unused here
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sample_packing: true # pack many short games per sequence (block-diagonal attn)
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eval_sample_packing: false
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# No LoRA — full fine-tuning
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wandb_project: othello-snake-ft
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wandb_entity: cfierro
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wandb_watch:
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wandb_name: llama-3.1-8b-fft-fixed-prefix-2e-5
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wandb_log_model: "false"
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# --- Multi-GPU: 4x A40 (48GB) per node ---
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# Full FT of an 8B model needs ZeRO-3 to shard weights+grads+optimizer.
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# Effective batch = 4 GPUs * micro 4 * grad_accum 1 = 8 packed sequences.
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# At seq_len 1024 the 50k games pack into ~5k sequences -> ~320 steps/epoch
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# (~960 steps over 3 epochs).
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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num_epochs: 3
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#max_steps: 500
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# Checkpoint + evaluate once per epoch. (Axolotl-idiomatic alternative if your
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# version prefers it: saves_per_epoch: 1 / evals_per_epoch: 1.)
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save_strategy: epoch
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eval_strategy: epoch # uses val_set_size 0.02 (else the val split is never evaluated)
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 2e-5
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.03
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save_total_limit: 3 # keep all per-epoch checkpoints
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weight_decay: 0.0
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special_tokens:
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pad_token: <|end_of_text|>
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# DeepSpeed ZeRO Stage 3 — shards weights, gradients, and optimizer across GPUs
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deepspeed: deepspeed_configs/zero3.json
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# Verify the loss mask before training:
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# axolotl preprocess axolotl_configs/fullft-othello-snake-8b.yaml --debug
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# Confirm the prefix tokens show -100 (masked) and the move tokens + <|end_of_text|> are trained.
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