98 lines
No EOL
3 KiB
YAML
98 lines
No EOL
3 KiB
YAML
experiment:
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name: "progressive_reasoning_large_model"
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base_model: "meta-llama/Llama-3.2-8B" # Or other whitelisted models
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output_dir: "./outputs"
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use_wandb: true
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wandb_project: "matsuo-llm-comp-2025"
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model:
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load_in_4bit: true # Enable 4-bit quantization for memory efficiency
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bnb_4bit_compute_dtype: "bfloat16"
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bnb_4bit_use_double_quant: true
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bnb_4bit_quant_type: "nf4"
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device_map: "auto"
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# Additional memory optimizations
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gradient_checkpointing: true
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use_flash_attention_2: true # If available
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progressive_stages:
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- name: "basic_cot"
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description: "Basic Chain-of-Thought reasoning"
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dataset_path: "./data/basic_cot/"
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adapter_config:
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r: 16 # Larger rank for bigger models
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lora_alpha: 32
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lora_dropout: 0.05
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target_modules: ["q_proj", "v_proj", "k_proj", "o_proj"]
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init_lora_weights: true # Identity initialization
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training:
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num_epochs: 1
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per_device_batch_size: 1 # Small batch size for large models
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gradient_accumulation_steps: 16 # Effective batch size = 16
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learning_rate: 2e-4
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warmup_steps: 100
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max_length: 2048
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fp16: false
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bf16: true
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max_grad_norm: 0.3
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weight_decay: 0.001
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save_steps: 50
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logging_steps: 10
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- name: "math_reasoning"
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description: "Mathematical reasoning with think tags"
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dataset_path: "./data/math_reasoning/"
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inherit_from: "basic_cot"
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adapter_config:
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r: 32 # Increase rank for more complex tasks
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lora_alpha: 64
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lora_dropout: 0.05
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target_modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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init_lora_weights: true
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training:
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num_epochs: 2
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per_device_batch_size: 1
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gradient_accumulation_steps: 32 # Effective batch size = 32
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learning_rate: 1e-4
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warmup_steps: 200
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max_length: 4096
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bf16: true
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max_grad_norm: 0.3
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weight_decay: 0.001
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- name: "complex_reasoning"
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description: "Complex multi-step reasoning"
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dataset_path: "./data/complex_reasoning/"
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inherit_from: "math_reasoning"
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adapter_config:
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r: 64 # Maximum rank for most complex tasks
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lora_alpha: 128
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lora_dropout: 0.05
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target_modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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init_lora_weights: true
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training:
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num_epochs: 2
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per_device_batch_size: 1
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gradient_accumulation_steps: 64 # Effective batch size = 64
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learning_rate: 5e-5
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warmup_steps: 300
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max_length: 8192
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bf16: true
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max_grad_norm: 0.3
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weight_decay: 0.001
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evaluation:
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benchmarks:
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- "HLE"
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- "Do-Not-Answer"
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save_results: true
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results_dir: "./outputs/evaluation_results"
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# Memory optimization settings
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optimization:
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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ddp_find_unused_parameters: false
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fsdp: "full_shard auto_wrap" # For multi-GPU setups
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fsdp_transformer_layer_cls_to_wrap: "LlamaDecoderLayer" |