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3B_pretrain.yaml

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+# Config for multi-device full finetuning in full_finetune_distributed.py
+# using a Llama3 8B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3-8B-Instruct --output-dir /tmp/Meta-Llama-3-8B-Instruct --hf-token <HF_TOKEN>
+#
+# To launch on 4 devices, run the following command from root:
+#   tune run --nproc_per_node 4 full_finetune_distributed --config llama3/8B_full
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 4 full_finetune_distributed --config llama3/8B_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+#
+# This config works best when the model is being fine-tuned on 2+ GPUs.
+# Single device full finetuning requires more memory optimizations. It's
+# best to use 8B_full_single_device.yaml for those cases
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_s_tokenizer
+  path: ../model_zoo_llama3.2/tokenizer.model
+  max_seq_len: 512
+
+# Dataset
+dataset:
+  _component_: torchtune.datasets.sound_completion_dataset
+  source: jan-hq/raw-speech-whispervq-v2-merged
+  max_seq_len: 512
+  split: train
+  column: text
+
+seed: 42
+shuffle: True
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_2.llama3_2_s_3b
+  # path: model_zoo/Llama3.1_s_8b_init
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointerSaveSteps
+  checkpoint_dir: ../model_zoo_llama3.2/llama3.2-s-3b-init
+  checkpoint_files: [
+    model-00001-of-00002.safetensors,
+    model-00002-of-00002.safetensors,
+  ]
+  recipe_checkpoint: null
+  output_dir: ../model_zoo_llama3.2/llama3.2-3B-s
+  model_type: LLAMA3_2
+resume_from_checkpoint: False
+save_every_n_steps: 1000
+max_checkpoints: 3
+# Fine-tuning arguments
+batch_size: 24
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 2
+compile: False
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW #change this to use adam_mini: torchtune.modules.optimizer.Adam_mini
+  weight_decay: 0.01
+  lr: 2e-4
+  fused: True
+lr_scheduler:
+  _component_: torchtune.modules.get_cosine_schedule_with_warmup
+  num_warmup_steps: 80
+
+loss:
+  _component_: torch.nn.CrossEntropyLoss
+
+fsdp:
+  cpu_offload: False
+
+# Training env
+device: cuda
+dtype: bf16
+
+# Memory management
+enable_activation_checkpointing: True
+memory_efficient_fsdp_wrap: True
+ac_mode: 'selective'
+
+
+# Logging
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+output_dir: ../model_zoo_llama3.2/llama3.2-3B-s-log/
+log_every_n_steps: 1
+log_peak_memory_stats: False