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test_stage:
obcq_modifiers:
LogarithmicEqualizationModifier:
mappings:
- - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
- re:.*input_layernorm
- - ['re:.*gate_proj', 're:.*up_proj']
- re:.*post_attention_layernorm
- - ['re:.*down_proj']
- re:.*up_proj
QuantizationModifier:
ignore: [LlamaRotaryEmbedding, LlamaRMSNorm, SiLUActivation, model.layers.30.mlp.down_proj,
model.layers.1.mlp.down_proj, model.layers.0.mlp.down_proj, model.layers.4.mlp.down_proj,
model.layers.8.mlp.down_proj, MatMulOutput_QK, MatMulOutput_PV, MatMulLeftInput_QK,
MatMulLeftInput_PV, MatMulRightInput_QK, MatMulRightInput_PV, QuantizableMatMul]
post_oneshot_calibration: true
scheme_overrides:
Linear:
weights: {num_bits: 8, symmetric: true, strategy: channel}
Embedding:
input_activations: null
weights: {num_bits: 8, symmetric: false}
SparseGPTModifier:
sparsity: 0.0
block_size: 128
sequential_update: false
quantize: true
percdamp: 0.01
prunen: 0
prunem: 0
targets: [model.layers.0, model.layers.1, model.layers.2, model.layers.3, model.layers.4,
model.layers.5, model.layers.6, model.layers.7, model.layers.8, model.layers.9, model.layers.10,
model.layers.11, model.layers.12, model.layers.13, model.layers.14, model.layers.15,
model.layers.16, model.layers.17, model.layers.18, model.layers.19, model.layers.20,
model.layers.21, model.layers.22, model.layers.23, model.layers.24, model.layers.25,
model.layers.26, model.layers.27, model.layers.28, model.layers.29, model.layers.30,
model.layers.31, lm_head]
target_ids: [attention_mask, position_ids]
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