Qwen3-30B-A3B-FP8 / config.json
Scott Macdonell
Duplicate from Qwen/Qwen3-30B-A3B-FP8
441e747 verified
{
"architectures": [
"Qwen3MoeForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"decoder_sparse_step": 1,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"max_position_embeddings": 40960,
"max_window_layers": 48,
"mlp_only_layers": [],
"model_type": "qwen3_moe",
"moe_intermediate_size": 768,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts": 128,
"num_experts_per_tok": 8,
"num_hidden_layers": 48,
"num_key_value_heads": 4,
"output_router_logits": false,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"router_aux_loss_coef": 0.001,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936,
"quantization_config": {
"activation_scheme": "dynamic",
"modules_to_not_convert": [
"lm_head",
"model.layers.0.input_layernorm",
"model.layers.0.mlp.gate",
"model.layers.0.post_attention_layernorm",
"model.layers.1.input_layernorm",
"model.layers.1.mlp.gate",
"model.layers.1.post_attention_layernorm",
"model.layers.2.input_layernorm",
"model.layers.2.mlp.gate",
"model.layers.2.post_attention_layernorm",
"model.layers.3.input_layernorm",
"model.layers.3.mlp.gate",
"model.layers.3.post_attention_layernorm",
"model.layers.4.input_layernorm",
"model.layers.4.mlp.gate",
"model.layers.4.post_attention_layernorm",
"model.layers.5.input_layernorm",
"model.layers.5.mlp.gate",
"model.layers.5.post_attention_layernorm",
"model.layers.6.input_layernorm",
"model.layers.6.mlp.gate",
"model.layers.6.post_attention_layernorm",
"model.layers.7.input_layernorm",
"model.layers.7.mlp.gate",
"model.layers.7.post_attention_layernorm",
"model.layers.8.input_layernorm",
"model.layers.8.mlp.gate",
"model.layers.8.post_attention_layernorm",
"model.layers.9.input_layernorm",
"model.layers.9.mlp.gate",
"model.layers.9.post_attention_layernorm",
"model.layers.10.input_layernorm",
"model.layers.10.mlp.gate",
"model.layers.10.post_attention_layernorm",
"model.layers.11.input_layernorm",
"model.layers.11.mlp.gate",
"model.layers.11.post_attention_layernorm",
"model.layers.12.input_layernorm",
"model.layers.12.mlp.gate",
"model.layers.12.post_attention_layernorm",
"model.layers.13.input_layernorm",
"model.layers.13.mlp.gate",
"model.layers.13.post_attention_layernorm",
"model.layers.14.input_layernorm",
"model.layers.14.mlp.gate",
"model.layers.14.post_attention_layernorm",
"model.layers.15.input_layernorm",
"model.layers.15.mlp.gate",
"model.layers.15.post_attention_layernorm",
"model.layers.16.input_layernorm",
"model.layers.16.mlp.gate",
"model.layers.16.post_attention_layernorm",
"model.layers.17.input_layernorm",
"model.layers.17.mlp.gate",
"model.layers.17.post_attention_layernorm",
"model.layers.18.input_layernorm",
"model.layers.18.mlp.gate",
"model.layers.18.post_attention_layernorm",
"model.layers.19.input_layernorm",
"model.layers.19.mlp.gate",
"model.layers.19.post_attention_layernorm",
"model.layers.20.input_layernorm",
"model.layers.20.mlp.gate",
"model.layers.20.post_attention_layernorm",
"model.layers.21.input_layernorm",
"model.layers.21.mlp.gate",
"model.layers.21.post_attention_layernorm",
"model.layers.22.input_layernorm",
"model.layers.22.mlp.gate",
"model.layers.22.post_attention_layernorm",
"model.layers.23.input_layernorm",
"model.layers.23.mlp.gate",
"model.layers.23.post_attention_layernorm",
"model.layers.24.input_layernorm",
"model.layers.24.mlp.gate",
"model.layers.24.post_attention_layernorm",
"model.layers.25.input_layernorm",
"model.layers.25.mlp.gate",
"model.layers.25.post_attention_layernorm",
"model.layers.26.input_layernorm",
"model.layers.26.mlp.gate",
"model.layers.26.post_attention_layernorm",
"model.layers.27.input_layernorm",
"model.layers.27.mlp.gate",
"model.layers.27.post_attention_layernorm",
"model.layers.28.input_layernorm",
"model.layers.28.mlp.gate",
"model.layers.28.post_attention_layernorm",
"model.layers.29.input_layernorm",
"model.layers.29.mlp.gate",
"model.layers.29.post_attention_layernorm",
"model.layers.30.input_layernorm",
"model.layers.30.mlp.gate",
"model.layers.30.post_attention_layernorm",
"model.layers.31.input_layernorm",
"model.layers.31.mlp.gate",
"model.layers.31.post_attention_layernorm",
"model.layers.32.input_layernorm",
"model.layers.32.mlp.gate",
"model.layers.32.post_attention_layernorm",
"model.layers.33.input_layernorm",
"model.layers.33.mlp.gate",
"model.layers.33.post_attention_layernorm",
"model.layers.34.input_layernorm",
"model.layers.34.mlp.gate",
"model.layers.34.post_attention_layernorm",
"model.layers.35.input_layernorm",
"model.layers.35.mlp.gate",
"model.layers.35.post_attention_layernorm",
"model.layers.36.input_layernorm",
"model.layers.36.mlp.gate",
"model.layers.36.post_attention_layernorm",
"model.layers.37.input_layernorm",
"model.layers.37.mlp.gate",
"model.layers.37.post_attention_layernorm",
"model.layers.38.input_layernorm",
"model.layers.38.mlp.gate",
"model.layers.38.post_attention_layernorm",
"model.layers.39.input_layernorm",
"model.layers.39.mlp.gate",
"model.layers.39.post_attention_layernorm",
"model.layers.40.input_layernorm",
"model.layers.40.mlp.gate",
"model.layers.40.post_attention_layernorm",
"model.layers.41.input_layernorm",
"model.layers.41.mlp.gate",
"model.layers.41.post_attention_layernorm",
"model.layers.42.input_layernorm",
"model.layers.42.mlp.gate",
"model.layers.42.post_attention_layernorm",
"model.layers.43.input_layernorm",
"model.layers.43.mlp.gate",
"model.layers.43.post_attention_layernorm",
"model.layers.44.input_layernorm",
"model.layers.44.mlp.gate",
"model.layers.44.post_attention_layernorm",
"model.layers.45.input_layernorm",
"model.layers.45.mlp.gate",
"model.layers.45.post_attention_layernorm",
"model.layers.46.input_layernorm",
"model.layers.46.mlp.gate",
"model.layers.46.post_attention_layernorm",
"model.layers.47.input_layernorm",
"model.layers.47.mlp.gate",
"model.layers.47.post_attention_layernorm"
],
"fmt": "e4m3",
"quant_method": "fp8",
"weight_block_size": [
128,
128
]
}
}