Add GLM5 (ml-explore#867)

* Add GLM4 MoE DSA model implementation with configurable parameters

* Update Acknowledgments to include GLM4 MoE DSA support

* format

* update ackn.

* Fixes

* Update acknowledgments to include contributions for GLM MoE DSA and additional architectures

* use dsv32 for glm5

* fix

* Fix rope theta

---------

Co-authored-by: Tarjei Mandt <kernelpool@gmail.com>
Co-authored-by: Awni Hannun <awni@apple.com>

Author: 3 people

ACKNOWLEDGMENTS.md

@@ -10,7 +10,7 @@ MLX LM was developed with contributions from the following individuals:
 - Shunta Saito: Added support for PLaMo models.
 - Gökdeniz Gülmez: Added support for the following architectures: 
 OpenBMB's `MiniCPM` and `MiniCPM3`, Kyutai's `Helium`, State-Space's `Mamba v1` and 
-`Mamba v2`, Z.ai & THUKEG's `GLM`, `GLM4`, Rednote `dots.llm1`, Baidu's `Ernie4.5 MoE`, 
+`Mamba v2`, Z.ai & THUKEG's `GLM`, `GLM4`, `GLM5 (GLM MoE DSA)`, Rednote `dots.llm1`, Baidu's `Ernie4.5 MoE`, 
 inclusionAI's `Bailing MoE e.g. Ling-family`, `Bailing MoE Linear e.g. Ling-Linear-family`, 
 Klear team - Kuaishou Technology's `Klear`, AI21 Lab's `Jamba` IBM's `Granite MoE`, 
 Meituan's `LongCat`, Nvidia's `Nemotron H`, Swiss-AI's `Apertus`, Nikity's `Lille130m`, 
@@ -26,4 +26,8 @@ Added support for the following other features:
   MoonshotAI's `Kimi-Linear`, LiquidAI's `LFM2` and `LFM2 MoE`, 
   Google DeepMind's `Gemma 3`, TII's `Falcon H1` and InterLM's `InternLM 2.5`.
 - Ivan Fioravanti: Added support for the following architectures:
- ServiceNow-AI's `Apriel 1.5`, Tencent's `Hunyuan Dense V1` and `Hunyuan MoE V1`.
+ ServiceNow-AI's `Apriel 1.5`, Tencent's `Hunyuan Dense V1` and `Hunyuan MoE V1`.
+- Tarjei Mandt: Added support for the following architectures: `Step 3.5 Flash`, 
+MoonshotAI's `Kimi K2.5`, Upstage's `Solar Open`, LG AI Research's `K-Exaone MoE`, 
+Meituan's `LongCat Flash Lite` Helped add support for the following model architectures: 
+Z.ai & THUKEG's `GLM5 (GLM MoE DSA)`

mlx_lm/models/deepseek_v32.py

@@ -71,7 +71,7 @@ def __init__(self, args: ModelArgs):
         self.rope = initialize_rope(
             dims=args.qk_rope_head_dim,
             base=args.rope_theta,
-            traditional=False,
+            traditional=True,
             max_position_embeddings=args.max_position_embeddings,
             scaling_config=args.rope_scaling,
         )
@@ -495,6 +495,16 @@ def __call__(
         return self.lm_head(out)
 
     def sanitize(self, weights):
+        # Remove multi-token prediction layers
+        mpt_layer = self.args.num_hidden_layers
+        new_weights = {}
+        for k, v in weights.items():
+            parts = k.split(".")
+            if len(parts) >= 3 and parts[1] == "layers" and int(parts[2]) >= mpt_layer:
+                continue
+            new_weights[k] = v
+        weights = new_weights
+
         def dequant(weight, scale_inv):
             dtype = mx.bfloat16
             weight = mx.from_fp8(weight, dtype=mx.bfloat16)
@@ -572,12 +582,7 @@ def dequant(weight, scale_inv):
                 weights[f"{prefix}.embed_q.weight"] = wk
                 weights[f"{prefix}.unembed_out.weight"] = wv
 
-        # Remove multi-token prediction layer and any unused precomputed rotary freqs
-        return {
-            k: v
-            for k, v in weights.items()
-            if not k.startswith("model.layers.61") and "rotary_emb.inv_freq" not in k
-        }
+        return weights
 
     def shard(self, group: Optional[mx.distributed.Group] = None):
         group = group or mx.distributed.init()

mlx_lm/models/glm_moe_dsa.py

@@ -0,0 +1,53 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from .base import BaseModelArgs
+from .deepseek_v32 import Model as DSV32Model
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    vocab_size: int
+    hidden_size: int
+    index_head_dim: int
+    index_n_heads: int
+    index_topk: int
+    intermediate_size: int
+    moe_intermediate_size: int
+    num_hidden_layers: int
+    num_attention_heads: int
+    num_key_value_heads: int
+    n_shared_experts: Optional[int]
+    n_routed_experts: Optional[int]
+    routed_scaling_factor: float
+    kv_lora_rank: int
+    q_lora_rank: int
+    qk_rope_head_dim: int
+    v_head_dim: int
+    qk_nope_head_dim: int
+    topk_method: str
+    scoring_func: str
+    norm_topk_prob: bool
+    n_group: int
+    topk_group: int
+    num_experts_per_tok: int
+    moe_layer_freq: int
+    first_k_dense_replace: int
+    max_position_embeddings: int
+    rms_norm_eps: float
+    rope_parameters: Dict
+    attention_bias: bool
+    rope_scaling: Dict = None
+    rope_theta: Optional[float] = None
+
+    def __post_init__(self):
+        self.rope_scaling = self.rope_parameters
+        self.rope_theta = self.rope_parameters["rope_theta"]
+
+
+class Model(DSV32Model):
+    def __init__(self, config: ModelArgs):
+        super().__init__(config)