✨ feat: add GLM-4-9B-Chat-1M model support

Add support for GLM-4 model architecture with the following changes:

- Add glm4.py with ChatGLMForCausalLM, GLM4Model, GLM4Attention, GLM4MLP
- Add GLM4RotaryEmbedding with interleaved partial rotation (rotary_dim = head_dim // 2)
- Add apply_rotary_emb_interleaved function for GLM-4 style RoPE
- Add GLM-4 weight name conversion and loading in loader.py
- Add GLM-4 chat template conversion in test_ruler.py
- Add trust_remote_code=True for GLM-4 config loading

Key GLM-4 specific adaptations:
- QKV bias enabled (add_qkv_bias: true)
- RoPE with rope_ratio scaling (base = 10000 * rope_ratio)
- Interleaved RoPE (pairs adjacent elements, not first/second half)
- Partial rotation (only half of head_dim is rotated)
- Uses multi_query_group_num instead of num_key_value_heads
- Uses kv_channels instead of head_dim
- Uses ffn_hidden_size instead of intermediate_size

Tested with RULER niah_single_1 (5 samples): 100% accuracy
Both GPU-only and CPU offload modes verified

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

nanovllm/config.py

@@ -57,8 +57,11 @@ class Config:
         assert os.path.isdir(self.model)
         assert self.kvcache_block_size % 256 == 0
         assert 1 <= self.tensor_parallel_size <= 8
-        self.hf_config = AutoConfig.from_pretrained(self.model)
-        self.max_model_len = min(self.max_model_len, self.hf_config.max_position_embeddings)
+        self.hf_config = AutoConfig.from_pretrained(self.model, trust_remote_code=True)
+        # Get max position embeddings (GLM-4 uses seq_length instead of max_position_embeddings)
+        max_pos = getattr(self.hf_config, 'max_position_embeddings',
+                         getattr(self.hf_config, 'seq_length', 4096))
+        self.max_model_len = min(self.max_model_len, max_pos)
         assert self.max_num_batched_tokens >= self.max_model_len
 
         # Override torch_dtype if user specified

nanovllm/engine/llm_engine.py

@@ -30,7 +30,7 @@ class LLMEngine:
             self.ps.append(process)
             self.events.append(event)
         self.model_runner = ModelRunner(config, 0, self.events)
-        self.tokenizer = AutoTokenizer.from_pretrained(config.model, use_fast=True)
+        self.tokenizer = AutoTokenizer.from_pretrained(config.model, use_fast=True, trust_remote_code=True)
         config.eos = self.tokenizer.eos_token_id
         # Set Sequence.block_size to match the KV cache block size
         Sequence.block_size = config.kvcache_block_size

nanovllm/engine/model_runner.py

@@ -30,6 +30,18 @@ def _find_free_port() -> int:
         return s.getsockname()[1]
 
 
+def get_num_kv_heads(hf_config) -> int:
+    """Get number of KV heads from config (handles GLM-4's multi_query_group_num)."""
+    return getattr(hf_config, 'num_key_value_heads',
+                   getattr(hf_config, 'multi_query_group_num', hf_config.num_attention_heads))
+
+
+def get_head_dim(hf_config) -> int:
+    """Get head dimension from config (handles GLM-4's kv_channels)."""
+    return getattr(hf_config, "head_dim",
+                   getattr(hf_config, "kv_channels", hf_config.hidden_size // hf_config.num_attention_heads))
+
+
 class ModelRunner:
 
     def __init__(self, config: Config, rank: int, event: Event | list[Event]):
@@ -144,8 +156,8 @@ class ModelRunner:
         used = total - free
         peak = torch.cuda.memory_stats()["allocated_bytes.all.peak"]
         current = torch.cuda.memory_stats()["allocated_bytes.all.current"]
-        num_kv_heads = hf_config.num_key_value_heads // self.world_size
-        head_dim = getattr(hf_config, "head_dim", hf_config.hidden_size // hf_config.num_attention_heads)
+        num_kv_heads = get_num_kv_heads(hf_config) // self.world_size
+        head_dim = get_head_dim(hf_config)
         block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * num_kv_heads * head_dim * hf_config.torch_dtype.itemsize
 
         # Calculate max GPU blocks based on available memory
@@ -787,8 +799,8 @@ class ModelRunner:
         - LastGraph: o_proj → post_norm → mlp → final_norm
         """
         hf_config = self.config.hf_config
-        num_kv_heads = hf_config.num_key_value_heads // self.world_size
-        head_dim = getattr(hf_config, "head_dim", hf_config.hidden_size // hf_config.num_attention_heads)
+        num_kv_heads = get_num_kv_heads(hf_config) // self.world_size
+        head_dim = get_head_dim(hf_config)
 
         # Create Decode Graph Manager (seq_len=1)
         self.decode_graph_manager = OffloadGraphManager(

nanovllm/layers/rotary_embedding.py

@@ -8,12 +8,43 @@ def apply_rotary_emb(
     cos: torch.Tensor,
     sin: torch.Tensor,
 ) -> torch.Tensor:
+    """Non-interleaved RoPE (used by Llama, Qwen, etc.)"""
     x1, x2 = torch.chunk(x.float(), 2, dim=-1)
     y1 = x1 * cos - x2 * sin
     y2 = x2 * cos + x1 * sin
     return torch.cat((y1, y2), dim=-1).to(x.dtype)
 
 
+def apply_rotary_emb_interleaved(
+    x: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> torch.Tensor:
+    """Interleaved RoPE (used by GLM-4, etc.)
+
+    Args:
+        x: [seq_len, num_heads, head_dim]
+        cos: [seq_len, 1, head_dim // 2]
+        sin: [seq_len, 1, head_dim // 2]
+
+    x is reshaped to [seq_len, num_heads, head_dim // 2, 2] where:
+    - x[..., 0] are even positions
+    - x[..., 1] are odd positions
+    """
+    rot_dim = x.shape[-1]
+    # x_shaped: [seq_len, num_heads, rot_dim // 2, 2]
+    x_shaped = x.float().reshape(*x.shape[:-1], rot_dim // 2, 2)
+    # x_0, x_1: [seq_len, num_heads, rot_dim // 2]
+    x_0 = x_shaped[..., 0]
+    x_1 = x_shaped[..., 1]
+    # cos/sin: [seq_len, 1, rot_dim // 2] - broadcasts to num_heads
+    x_out = torch.stack([
+        x_0 * cos - x_1 * sin,
+        x_1 * cos + x_0 * sin,
+    ], dim=-1)
+    return x_out.flatten(-2).to(x.dtype)
+
+
 class RotaryEmbedding(nn.Module):
 
     def __init__(
@@ -140,6 +171,76 @@ class Llama3RotaryEmbedding(nn.Module):
         return query, key
 
 
+class GLM4RotaryEmbedding(nn.Module):
+    """
+    GLM-4 RoPE with interleaved rotation and partial rotation.
+
+    GLM-4 uses:
+    - Interleaved rotation (pairs adjacent elements, not first/second half)
+    - rope_ratio to scale base: base = 10000 * rope_ratio
+    - Partial rotation: only rotates first rotary_dim elements, rest pass through
+    - rotary_dim = head_dim // 2 (only half of head_dim is rotated)
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: float,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim  # GLM-4: rotary_dim = head_dim // 2
+        # inv_freq shape: [rotary_dim // 2]
+        inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2, dtype=torch.float) / rotary_dim))
+        t = torch.arange(max_position_embeddings, dtype=torch.float)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)  # [max_pos, rotary_dim // 2]
+        cos = freqs.cos()
+        sin = freqs.sin()
+        # cache shape [max_pos, 1, rotary_dim // 2, 2]
+        cache = torch.stack((cos, sin), dim=-1).unsqueeze_(1)
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    @torch.compile
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Apply RoPE to query and key.
+
+        Args:
+            positions: [seq_len]
+            query: [seq_len, num_heads, head_dim]
+            key: [seq_len, num_kv_heads, head_dim]
+
+        Returns:
+            Rotated query and key with same shapes as input.
+        """
+        cache = self.cos_sin_cache[positions]  # [seq_len, 1, rotary_dim // 2, 2]
+        cos = cache[..., 0]  # [seq_len, 1, rotary_dim // 2]
+        sin = cache[..., 1]  # [seq_len, 1, rotary_dim // 2]
+
+        # Split into rotated and pass-through parts
+        q_rot = query[..., :self.rotary_dim]
+        q_pass = query[..., self.rotary_dim:]
+        k_rot = key[..., :self.rotary_dim]
+        k_pass = key[..., self.rotary_dim:]
+
+        # Apply interleaved RoPE to rotated part
+        q_rot = apply_rotary_emb_interleaved(q_rot, cos, sin)
+        k_rot = apply_rotary_emb_interleaved(k_rot, cos, sin)
+
+        # Concatenate rotated and pass-through parts
+        query = torch.cat([q_rot, q_pass], dim=-1)
+        key = torch.cat([k_rot, k_pass], dim=-1)
+
+        return query, key
+
+
 # Cache for RoPE instances (keyed by hashable parameters)
 _rope_cache: dict[tuple, nn.Module] = {}
 
@@ -150,10 +251,11 @@ def get_rope(
     max_position: int,
     base: float,
     rope_scaling: dict | None = None,
+    is_interleaved: bool = False,
 ):
     # Create hashable cache key
     if rope_scaling is None:
-        cache_key = (head_size, rotary_dim, max_position, base, None)
+        cache_key = (head_size, rotary_dim, max_position, base, None, is_interleaved)
     else:
         rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
         if rope_type == "llama3":
@@ -163,15 +265,19 @@ def get_rope(
                 rope_scaling["low_freq_factor"],
                 rope_scaling["high_freq_factor"],
                 rope_scaling["original_max_position_embeddings"],
+                is_interleaved,
             )
         else:
-            cache_key = (head_size, rotary_dim, max_position, base, rope_type)
+            cache_key = (head_size, rotary_dim, max_position, base, rope_type, is_interleaved)
 
     if cache_key in _rope_cache:
         return _rope_cache[cache_key]
 
     if rope_scaling is None:
-        rope = RotaryEmbedding(head_size, rotary_dim, max_position, base)
+        if is_interleaved:
+            rope = GLM4RotaryEmbedding(head_size, rotary_dim, max_position, base)
+        else:
+            rope = RotaryEmbedding(head_size, rotary_dim, max_position, base)
     else:
         rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
         if rope_type == "llama3":

nanovllm/models/__init__.py

@@ -5,5 +5,6 @@ from nanovllm.models.registry import register_model, get_model_class, MODEL_REGI
 # Import models to trigger registration
 from nanovllm.models import qwen3
 from nanovllm.models import llama
+from nanovllm.models import glm4
 
 __all__ = ["register_model", "get_model_class", "MODEL_REGISTRY"]

nanovllm/models/glm4.py

@@ -0,0 +1,235 @@
+"""GLM-4 model implementation for nano-vllm."""
+import torch
+from torch import nn
+import torch.distributed as dist
+
+from nanovllm.layers.activation import SiluAndMul
+from nanovllm.layers.attention import Attention
+from nanovllm.layers.layernorm import RMSNorm
+from nanovllm.layers.linear import QKVParallelLinear, MergedColumnParallelLinear, RowParallelLinear
+from nanovllm.layers.rotary_embedding import get_rope
+from nanovllm.layers.embed_head import VocabParallelEmbedding, ParallelLMHead
+from nanovllm.models.registry import register_model
+
+
+class GLM4Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 1048576,
+        head_dim: int = 128,
+        rope_theta: float = 10000,
+        rope_scaling: dict | None = None,
+    ) -> None:
+        super().__init__()
+        tp_size = dist.get_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim ** -0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=True,  # GLM-4 has QKV bias
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,  # GLM-4 has no output bias
+        )
+        # GLM-4 only rotates half of head_dim
+        rotary_dim = self.head_dim // 2
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=rotary_dim,
+            max_position=max_position,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            is_interleaved=True,  # GLM-4 uses interleaved RoPE
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            self.num_kv_heads,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = q.view(-1, self.num_heads, self.head_dim)
+        k = k.view(-1, self.num_kv_heads, self.head_dim)
+        v = v.view(-1, self.num_kv_heads, self.head_dim)
+        q, k = self.rotary_emb(positions, q, k)
+        o = self.attn(q, k, v)
+        output = self.o_proj(o.flatten(1, -1))
+        return output
+
+
+class GLM4MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,  # GLM-4 has no MLP bias
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x = self.down_proj(x)
+        return x
+
+
+class GLM4DecoderLayer(nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+        # GLM-4 config field mapping
+        hidden_size = config.hidden_size
+        num_heads = config.num_attention_heads
+        num_kv_heads = getattr(config, 'multi_query_group_num', num_heads)
+        head_dim = getattr(config, 'kv_channels', hidden_size // num_heads)
+        max_position = getattr(config, 'seq_length', 1048576)
+        rope_ratio = getattr(config, 'rope_ratio', 1)
+        rope_theta = 10000 * rope_ratio  # GLM-4 uses rope_ratio to scale base
+        intermediate_size = getattr(config, 'ffn_hidden_size', getattr(config, 'intermediate_size', None))
+        rms_norm_eps = getattr(config, 'layernorm_epsilon', 1e-5)
+
+        self.self_attn = GLM4Attention(
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            max_position=max_position,
+            head_dim=head_dim,
+            rope_theta=rope_theta,
+            rope_scaling=getattr(config, "rope_scaling", None),
+        )
+        self.mlp = GLM4MLP(
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+        )
+        self.input_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            hidden_states, residual = self.input_layernorm(hidden_states), hidden_states
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+        hidden_states = self.self_attn(positions, hidden_states)
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class GLM4Model(nn.Module):
+
+    def __init__(self, config) -> None:
+        super().__init__()
+        vocab_size = getattr(config, 'padded_vocab_size', config.vocab_size)
+        num_layers = getattr(config, 'num_layers', config.num_hidden_layers)
+        rms_norm_eps = getattr(config, 'layernorm_epsilon', 1e-5)
+
+        self.embed_tokens = VocabParallelEmbedding(vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([GLM4DecoderLayer(config) for _ in range(num_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+@register_model("ChatGLMModel", "ChatGLMForConditionalGeneration")
+class ChatGLMForCausalLM(nn.Module):
+    """
+    GLM-4 model for causal language modeling.
+
+    Weight mapping from HuggingFace to nanovllm:
+    - transformer.embedding.word_embeddings → model.embed_tokens
+    - transformer.encoder.layers.X.input_layernorm → model.layers.X.input_layernorm
+    - transformer.encoder.layers.X.self_attention.query_key_value → model.layers.X.self_attn.qkv_proj (split q/k/v)
+    - transformer.encoder.layers.X.self_attention.dense → model.layers.X.self_attn.o_proj
+    - transformer.encoder.layers.X.post_attention_layernorm → model.layers.X.post_attention_layernorm
+    - transformer.encoder.layers.X.mlp.dense_h_to_4h → model.layers.X.mlp.gate_up_proj (split gate/up)
+    - transformer.encoder.layers.X.mlp.dense_4h_to_h → model.layers.X.mlp.down_proj
+    - transformer.encoder.final_layernorm → model.norm
+    - transformer.output_layer → lm_head
+    """
+    packed_modules_mapping = {
+        # QKV is merged in GLM-4 as query_key_value
+        "query_key_value": ("qkv_proj", None),  # Special handling needed
+        # MLP gate and up are merged as dense_h_to_4h
+        "dense_h_to_4h": ("gate_up_proj", None),  # Special handling needed
+    }
+
+    # Weight name mapping for loader
+    hf_to_nanovllm_mapping = {
+        "transformer.embedding.word_embeddings": "model.embed_tokens",
+        "transformer.encoder.final_layernorm": "model.norm",
+        "transformer.output_layer": "lm_head",
+    }
+
+    def __init__(self, config) -> None:
+        super().__init__()
+        vocab_size = getattr(config, 'padded_vocab_size', config.vocab_size)
+        self.config = config
+        self.model = GLM4Model(config)
+        self.lm_head = ParallelLMHead(vocab_size, config.hidden_size)
+        # GLM-4 does not tie embeddings
+        # if getattr(config, 'tie_word_embeddings', False):
+        #     self.lm_head.weight.data = self.model.embed_tokens.weight.data
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.model(input_ids, positions)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.lm_head(hidden_states)

nanovllm/utils/loader.py

@@ -1,4 +1,5 @@
 import os
+import re
 from glob import glob
 import torch
 from torch import nn
@@ -9,20 +10,146 @@ def default_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor):
     param.data.copy_(loaded_weight)
 
 
+# GLM-4 weight name mappings
+GLM4_NAME_MAPPING = {
+    "transformer.embedding.word_embeddings": "model.embed_tokens",
+    "transformer.encoder.final_layernorm": "model.norm",
+    "transformer.output_layer": "lm_head",
+}
+
+GLM4_LAYER_MAPPING = {
+    "self_attention.query_key_value": "self_attn.qkv_proj",
+    "self_attention.dense": "self_attn.o_proj",
+    "mlp.dense_h_to_4h": "mlp.gate_up_proj",
+    "mlp.dense_4h_to_h": "mlp.down_proj",
+}
+
+
+def convert_glm4_weight_name(weight_name: str) -> tuple[str, str | None]:
+    """
+    Convert GLM-4 weight name to nanovllm format.
+
+    Returns:
+        tuple: (converted_name, shard_id) where shard_id is used for packed modules
+               Returns (None, None) for weights that should be skipped
+    """
+    # Skip rotary embedding weights (we use our own RoPE implementation)
+    if "rotary_pos_emb" in weight_name:
+        return None, None
+
+    # Check direct mappings first
+    for glm_name, nano_name in GLM4_NAME_MAPPING.items():
+        if weight_name.startswith(glm_name):
+            return weight_name.replace(glm_name, nano_name), None
+
+    # Handle layer weights: transformer.encoder.layers.X.xxx
+    layer_match = re.match(r"transformer\.encoder\.layers\.(\d+)\.(.+)", weight_name)
+    if layer_match:
+        layer_idx = layer_match.group(1)
+        remainder = layer_match.group(2)
+
+        # Handle packed modules (QKV and gate_up)
+        for glm_subname, nano_subname in GLM4_LAYER_MAPPING.items():
+            if remainder.startswith(glm_subname):
+                suffix = remainder[len(glm_subname):]  # .weight or .bias
+                new_name = f"model.layers.{layer_idx}.{nano_subname}{suffix}"
+
+                # Determine shard_id for packed modules
+                if "qkv_proj" in nano_subname:
+                    return new_name, "qkv"  # Special marker for GLM4 QKV
+                elif "gate_up_proj" in nano_subname:
+                    return new_name, "gate_up"  # Special marker for GLM4 gate_up
+                else:
+                    return new_name, None
+
+        # Handle non-packed layer weights (layernorms)
+        new_name = f"model.layers.{layer_idx}.{remainder}"
+        return new_name, None
+
+    # No mapping found, return original
+    return weight_name, None
+
+
+def load_glm4_qkv(param: nn.Parameter, loaded_weight: torch.Tensor, config):
+    """Load GLM-4 merged QKV weights by splitting into q, k, v."""
+    num_heads = config.num_attention_heads
+    num_kv_heads = getattr(config, 'multi_query_group_num', num_heads)
+    head_dim = getattr(config, 'kv_channels', config.hidden_size // num_heads)
+
+    q_size = num_heads * head_dim
+    kv_size = num_kv_heads * head_dim
+
+    # Split QKV: [q_size + kv_size + kv_size, hidden_size]
+    q, k, v = loaded_weight.split([q_size, kv_size, kv_size], dim=0)
+
+    # Load each part using the weight_loader
+    weight_loader = getattr(param, "weight_loader")
+    weight_loader(param, q, "q")
+    weight_loader(param, k, "k")
+    weight_loader(param, v, "v")
+
+
+def load_glm4_gate_up(param: nn.Parameter, loaded_weight: torch.Tensor, config):
+    """Load GLM-4 merged gate_up weights by splitting into gate, up."""
+    ffn_hidden_size = getattr(config, 'ffn_hidden_size', getattr(config, 'intermediate_size', None))
+
+    # Split gate_up: [ffn_hidden_size * 2, hidden_size]
+    gate, up = loaded_weight.split([ffn_hidden_size, ffn_hidden_size], dim=0)
+
+    # Load each part using the weight_loader
+    weight_loader = getattr(param, "weight_loader")
+    weight_loader(param, gate, 0)  # gate_proj is shard 0
+    weight_loader(param, up, 1)    # up_proj is shard 1
+
+
+def is_glm4_model(model: nn.Module) -> bool:
+    """Check if the model is a GLM-4 model."""
+    return model.__class__.__name__ in ("ChatGLMForCausalLM",)
+
+
 def load_model(model: nn.Module, path: str):
     packed_modules_mapping = getattr(model, "packed_modules_mapping", {})
+    is_glm4 = is_glm4_model(model)
+    config = getattr(model, "config", None)
+
     for file in glob(os.path.join(path, "*.safetensors")):
         with safe_open(file, "pt", "cpu") as f:
             for weight_name in f.keys():
+                loaded_weight = f.get_tensor(weight_name)
+
+                # GLM-4 specific handling
+                if is_glm4:
+                    param_name, shard_id = convert_glm4_weight_name(weight_name)
+
+                    # Skip weights that don't need to be loaded
+                    if param_name is None:
+                        continue
+
+                    if shard_id == "qkv":
+                        param = model.get_parameter(param_name)
+                        load_glm4_qkv(param, loaded_weight, config)
+                        continue
+                    elif shard_id == "gate_up":
+                        param = model.get_parameter(param_name)
+                        load_glm4_gate_up(param, loaded_weight, config)
+                        continue
+                    else:
+                        # Regular weight, use converted name
+                        param = model.get_parameter(param_name)
+                        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                        weight_loader(param, loaded_weight)
+                        continue
+
+                # Original loading logic for other models
                 for k in packed_modules_mapping:
                     if k in weight_name:
                         v, shard_id = packed_modules_mapping[k]
                         param_name = weight_name.replace(k, v)
                         param = model.get_parameter(param_name)
                         weight_loader = getattr(param, "weight_loader")
-                        weight_loader(param, f.get_tensor(weight_name), shard_id)
+                        weight_loader(param, loaded_weight, shard_id)
                         break
                 else:
                     param = model.get_parameter(weight_name)
                     weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                    weight_loader(param, f.get_tensor(weight_name))
+                    weight_loader(param, loaded_weight)