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Model: cfierro/llama-3.1-8b-fft-othello-snake-fixed-prefix-2e-5
Source: Original Platform

train_config.yaml

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