GLM-5 · Logits · OpenModelAtlas

Logits

tensorlogits

Unnormalized vocabulary scores for the token following each sequence position.

shape: $L$ [B batch, T positions, 154880 vocabulary]

Sources

Contextual evidence from Vocabulary projection

The model embeds tokens, numbers positions from the cached length, always builds the causal mask (the indexer needs it), applies the 78 blocks, normalizes and projects to vocabulary logits with an untied lm_head without upcasting them. The checkpoint’s layer 78, the multi-token-prediction layer, is ignored on load.

transformer · huggingface/transformers / modeling_glm_moe_dsa.py
Pinned revision 5e4d6304de55 ↗ · lines 648–802

Read code excerpt

    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.78.*"]

… lines 649–695 omitted …

        if inputs_embeds is None:
            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
                "allow_is_causal_skip": False,  # Always force creation to account for causality in the indexer
            }
            causal_mask_mapping = {"indexed_attention": create_causal_mask(**mask_kwargs)}

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        topk_indices = None  # MAIN DIFF with DSV3.2
        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            hidden_states, topk_indices = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping["indexed_attention"],
                position_embeddings=position_embeddings,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                prev_topk_indices=topk_indices,  # MAIN DIFF with DSV3.2
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )

… lines 740–748 omitted …

    def __init__(self, config):
        super().__init__(config)
        self.model = GlmMoeDsaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

… lines 754–798 omitted …

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

Contextual evidence from Vocabulary projection

The released configuration: width, 78 layers of which 3 dense, 64 heads, 256 routed experts and one shared, routing (sigmoid scores, one expert group, top-8, route scale 2.5), MLA ranks and head widths, the indexer (32 heads of 128, top 2,048, interleaved RoPE), vocabulary, default RoPE with θ = 1,000,000, and one multi-token-prediction layer.

config · zai-org/GLM-5 / config.json
Pinned revision c183ef8c61fa ↗ · lines 1–59

Read code excerpt

{
  "architectures": [
    "GlmMoeDsaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "dtype": "bfloat16",
  "eos_token_id": [
    154820,
    154827,
    154829
  ],
  "ep_size": 1,
  "first_k_dense_replace": 3,
  "hidden_act": "silu",
  "head_dim": 64,
  "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,
  "moe_intermediate_size": 2048,
  "moe_layer_freq": 1,
  "model_type": "glm_moe_dsa",
  "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": "5.0.2.dev0",
  "use_cache": true,
  "v_head_dim": 256,
  "vocab_size": 154880
}