config.json

{
  "architectures": [
    "GlmMoeDsaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 0,
  "dtype": "bfloat16",
  "eos_token_id": [
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  "ep_size": 1,
  "first_k_dense_replace": 3,
  "hidden_act": "silu",
  "hidden_size": 6144,
  "index_head_dim": 128,
  "index_n_heads": 32,
  "index_topk": 2048,
  "indexer_rope_interleave": true,
  "initializer_range": 0.02,
  "intermediate_size": 12288,
  "kv_lora_rank": 512,
  "max_position_embeddings": 202752,
  "mlp_layer_types": [
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  ],
  "model_type": "glm_moe_dsa",
  "moe_intermediate_size": 2048,
  "moe_layer_freq": 1,
  "n_group": 1,
  "n_routed_experts": 256,
  "n_shared_experts": 1,
  "norm_topk_prob": true,
  "num_attention_heads": 64,
  "num_experts_per_tok": 8,
  "num_hidden_layers": 78,
  "num_key_value_heads": 64,
  "num_nextn_predict_layers": 1,
  "pad_token_id": 154820,
  "pretraining_tp": 1,
  "q_lora_rank": 2048,
  "qk_head_dim": 256,
  "qk_nope_head_dim": 192,
  "qk_rope_head_dim": 64,
  "rms_norm_eps": 1e-05,
  "rope_interleave": true,
  "rope_parameters": {
    "rope_theta": 1000000,
    "rope_type": "default"
  },
  "routed_scaling_factor": 2.5,
  "scoring_func": "sigmoid",
  "tie_word_embeddings": false,
  "topk_group": 1,
  "topk_method": "noaux_tc",
  "transformers_version": "5.5.3",
  "use_cache": true,
  "v_head_dim": 256,
  "vocab_size": 154880,
  "quantization_config": {
    "quant_method": "compressed-tensors",
    "quantization_status": "compressed",
    "format": "float-quantized",
    "config_groups": {
      "group_fp8": {
        "format": "float-quantized",
        "targets": [
          "Linear"
        ],
        "weights": {
          "num_bits": 8,
          "type": "float",
          "symmetric": true,
          "strategy": "channel",
          "dynamic": false
        },
        "input_activations": {
          "num_bits": 8,
          "type": "float",
          "symmetric": true,
          "strategy": "token",
          "dynamic": true
        }
      }
    },
    "ignore": [
      "lm_head",
      "model.embed_tokens",
      "re:.*\\.mlp\\.gate$",
      "re:.*norm.*",
      "re:.*layernorm.*",
      "re:.*\\.indexer\\.k_norm.*"
    ]
  }
}