model: repository: "tomg-group-umd/huginn-0125" revision: "bb6621b65e90b6a4b9b29ef88dc83866d450470c" config_sha256: "e9fe79df06a783ca33a76038c59a715b6a62f15c4a5b17b9681e697fea46c79c" modeling_sha256: "a1d447da93c605a6dc11fbc17cbc7185663f4a41ae12335b34b5847edf0f84aa" physical_layers: 4 recurrent_loops: 32 hidden_size: 5280 intermediate_size: 17920 groups: attention_qkv: hf_weights: [attn.Wqkv] vllm_consumer: self_attn.qkv_proj attention_output: hf_weights: [attn.proj] vllm_consumer: self_attn.o_proj mlp_up_gate: hf_weights: [mlp.fc] vllm_consumer: mlp.gate_up_proj mlp_down: hf_weights: [mlp.proj] vllm_consumer: mlp.down_proj slt_budget: rule: "ceil(5% of candidate transform parameters), then select whole groups until the target is met" fixed_group_count: null status: "requires calibrated transform factorization and candidate parameter counts" runtime_status: adapter_contract: implemented vllm_hook_integration: implemented_cpu_validated activation_sites: [attention_qkv, attention_output, mlp_up_gate, mlp_down] weight_dispatch: "shared W4 plus recurrence-indexed variants for selected SLT groups" cta_boundaries: 31 method_isolation: "reject NVFP4, ResidualQuant, MomentumQuant, and residual activation policies" artifact_export: >- Implemented with a mandatory progressive SLT audit using the 5% parameter target and whole-group overshoot rule; arbitrary fixed group counts are rejected. calibration: implemented_cpu_validated_gpu_not_run calibration_command: >- CUDA_VISIBLE_DEVICES=0 PYTHONPATH=./loopQ python loopQ/scripts/calibrate_huginn.py --gpu 0 --optimizer adamw --learning-rate 5e-3 --total-steps 1500 --slt-round-steps 25 --hidden-factors 60,88 --intermediate-factors 128,140 --output ${LOOPTRANSFORMER_DATA_ROOT}/checkpoints/loopq/huginn_w4a4_components.pt smoke_generation: not_run smoke_command: >- CUDA_VISIBLE_DEVICES=0 PYTHONPATH=./loopQ python loopQ/scripts/smoke_huginn_vllm.py --artifact ${LOOPTRANSFORMER_DATA_ROOT}/models/loopq/huginn_w4a4.pt --gpu 0