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config.yaml
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# ============================================================================
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# CyberLLM — 350M Parameter Configuration (Stretch Goal / v0.5 Target)
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# Architecture: LLaMA-3 Style (Decoder-Only Transformer)
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# ============================================================================
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model:
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name: "cyberllm-350m"
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architecture: "llama"
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vocab_size: 32000
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hidden_size: 1024
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num_layers: 24
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max_position_embeddings: 4096
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num_attention_heads: 16
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num_kv_heads: 4 # GQA ratio: 16/4 = 4
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head_dim: 64
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intermediate_size: 2816 # SwiGLU FFN dim
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hidden_act: "silu"
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norm_type: "rmsnorm"
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rms_norm_eps: 1.0e-5
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position_encoding: "rope"
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rope_theta: 10000.0
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rope_scaling: null
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tie_word_embeddings: true
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attention_dropout: 0.0
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hidden_dropout: 0.0
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initializer_range: 0.02
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training:
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optimizer: "adamw"
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learning_rate: 3.0e-4
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min_learning_rate: 3.0e-5
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weight_decay: 0.1
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adam_beta1: 0.9
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adam_beta2: 0.95
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adam_epsilon: 1.0e-8
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max_grad_norm: 1.0
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lr_scheduler: "cosine"
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warmup_steps: 500
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total_tokens: 5_000_000_000 # 5B tokens (~2 epochs)
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micro_batch_size: 2 # Fits A40 48GB
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gradient_accumulation_steps: 64 # Effective batch = 262K tokens/step
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sequence_length: 2048 # 2048 for A40 memory
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mixed_precision: "bf16"
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save_interval_steps: 500
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eval_interval_steps: 250
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log_interval_steps: 10
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keep_last_n_checkpoints: 3
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data_mix:
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stage_1:
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general_text: 0.60
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security_text: 0.30
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code: 0.10
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stage_2:
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general_text: 0.30
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security_text: 0.55
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code: 0.15
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stage_3:
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general_text: 0.15
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security_text: 0.70
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code: 0.15
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infrastructure:
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target_gpu: "a40_48gb"
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num_gpus: 1
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estimated_time_hours: 50
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estimated_cost_usd: 10
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local_device: "mps"
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local_test_batch_size: 1
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local_test_seq_length: 128
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# Expected parameter count: ~303M
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# Tokens per step: 2 * 64 * 2048 = 262,144
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# Total steps: ~19,073
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# A40 throughput: ~20-30K tok/s → ~50 hours
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# A100 throughput: ~50-70K tok/s → ~20 hours
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