diff --git a/ACKNOWLEDGMENTS.md b/ACKNOWLEDGMENTS.md
index a2dd8cbd7..63772aaf0 100644
--- a/ACKNOWLEDGMENTS.md
+++ b/ACKNOWLEDGMENTS.md
@@ -13,11 +13,11 @@ MLX LM was developed with contributions from the following individuals:
    THUKEG's `GLM4`, Rednote `dots.llm1`, Baisu's `Ernie4.5 MoE`, inclusionAI's
    `Bailing MoE e.g. Ling-family`, Klear team - Kuaishou Technology's `Klear`,
    IBM's `Granite MoE`, Meituan's `LongCat`, Nvidia's `Nemotron H`, Swiss-AI's
-   `Apertus`, Nikity's `Lille130m`, and Allenai's `OLMoE`; Added support for the
-   following training algorithms: `Full Weight Fine-Tuning`, and the `Muon`
+   `Apertus`, Nikity's `Lille130m`, Alibaba Qwen's `Qwen3Next`, and Allenai's `OLMoE`;
+   Helped add support for the following model architectures: Alibaba Qwen's `Qwen3 & Qwen3MoE)`;
+   Added support for the following training algorithms: `Full Weight Fine-Tuning`, and the `Muon`
    optimizer; Added support for the following other features: `Multiple Optimizers
-   to choose for training`, and `reporting training metrics to WandB (Weights &
-   Biases)`.
+   to choose for training`, and `reporting training metrics to WandB (Weights & Biases)`.
 - Prince Canuma: Helped add support for the following model architectures:
   HuggingFace's `Starcoder2`, Cohere's `Cohere (1 and 2)`, Alibaba Qwen's `Qwen
   (2, 3 and MoE)`, Microsoft's `Phi (3 and 3.5 MoE)`, `BitNet1.58`, Meta's `Llama
diff --git a/mlx_lm/models/qwen3_moe.py b/mlx_lm/models/qwen3_moe.py
index eea1f42c6..d94758a7a 100644
--- a/mlx_lm/models/qwen3_moe.py
+++ b/mlx_lm/models/qwen3_moe.py
@@ -127,7 +127,7 @@ def __call__(
         gates = mx.softmax(gates, axis=-1, precise=True)
 
         k = self.top_k
-        inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k])
+        inds = mx.argpartition(gates, kth=-k, axis=-1)[..., -k:]
         scores = mx.take_along_axis(gates, inds, axis=-1)
         if self.norm_topk_prob:
             scores /= mx.sum(scores, axis=-1, keepdims=True)
diff --git a/mlx_lm/models/qwen3_next.py b/mlx_lm/models/qwen3_next.py
new file mode 100644
index 000000000..19ea7c2d2
--- /dev/null
+++ b/mlx_lm/models/qwen3_next.py
@@ -0,0 +1,496 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .cache import KVCache, MambaCache
+from .rope_utils import initialize_rope
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    linear_num_value_heads: int
+    linear_num_key_heads: int
+    linear_key_head_dim: int
+    linear_value_head_dim: int
+    linear_conv_kernel_dim: int
+    num_experts: int
+    num_experts_per_tok: int
+    decoder_sparse_step: int
+    shared_expert_intermediate_size: int
+    mlp_only_layers: List[int]
+    moe_intermediate_size: int
+    rms_norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int
+    rope_theta: float
+    partial_rotary_factor: float
+    max_position_embeddings: int
+    norm_topk_prob: bool = False
+    tie_word_embeddings: bool = False
+    attention_bias: bool = False
+    head_dim: Optional[int] = None
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    full_attention_interval: int = 4
+
+
+@partial(mx.compile, shapeless=True)
+def compute_g(A_log, a, dt_bias):
+    return mx.exp(-mx.exp(A_log.astype(mx.float32)) * nn.softplus(a + dt_bias)).astype(
+        A_log.dtype
+    )
+
+
+def recurrent_gated_delta_rule(
+    query: mx.array,
+    key: mx.array,
+    value: mx.array,
+    a: mx.array,
+    b: mx.array,
+    A_log: mx.array,
+    dt_bias: mx.array,
+    state: mx.array,
+    use_qk_l2norm_in_kernel: bool = False,
+) -> Tuple[mx.array, mx.array]:
+    B, S, Hk, Dk = key.shape
+    Hv, Dv = value.shape[2:]
+    inv_scale = Dk**-0.5
+    if use_qk_l2norm_in_kernel:
+        query = (inv_scale**2) * mx.fast.rms_norm(query, None, 1e-6)
+        key = inv_scale * mx.fast.rms_norm(key, None, 1e-6)
+    else:
+        query = inv_scale * query
+
+    input_type = query.dtype
+    if (repeat_factor := Hv // Hk) > 1:
+        query = mx.repeat(query, repeat_factor, 2)
+        key = mx.repeat(key, repeat_factor, 2)
+
+    beta = mx.sigmoid(b)
+    g = compute_g(A_log, a, dt_bias)
+    outs = []
+    for i in range(S):
+        kv_mem = (state * key[:, i, :, :, None]).sum(axis=-2)
+        delta = (value[:, i] - kv_mem) * beta[:, i, :, None]
+        state += key[:, i, :, :, None] * delta[..., None, :]
+        out = (state * query[:, i, :, :, None]).sum(axis=-2)
+        outs.append(out)
+    out = mx.stack(outs, axis=1).astype(input_type)
+    return out, state
+
+
+class Qwen3NextRMSNormGated(nn.Module):
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = mx.ones(hidden_size)
+
+    def __call__(
+        self, hidden_states: mx.array, gate: mx.array | None = None
+    ) -> mx.array:
+        x = mx.fast.rms_norm(hidden_states, self.weight, self.eps)
+        if gate is not None:
+            x = x * nn.silu(gate)
+        return x
+
+
+class Qwen3NextAttention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_key_value_heads = args.num_key_value_heads
+        self.num_attention_heads = args.num_attention_heads
+        self.head_dim = args.head_dim
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(
+            args.hidden_size,
+            self.num_attention_heads * self.head_dim * 2,
+            bias=args.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            args.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=args.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            args.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=args.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            self.num_attention_heads * self.head_dim,
+            args.hidden_size,
+            bias=args.attention_bias,
+        )
+
+        self.q_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+        self.k_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
+
+        self.rope = initialize_rope(
+            int(self.head_dim * args.partial_rotary_factor),
+            base=args.rope_theta,
+            traditional=False,
+            scaling_config=args.rope_scaling,
+            max_position_embeddings=args.max_position_embeddings,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        q_proj_output = self.q_proj(x)
+        queries, gate = mx.split(
+            q_proj_output.reshape(B, L, self.num_attention_heads, -1), 2, axis=-1
+        )
+        gate = gate.reshape(B, L, -1)
+
+        keys, values = self.k_proj(x), self.v_proj(x)
+
+        queries = self.q_norm(queries).transpose(0, 2, 1, 3)
+        keys = self.k_norm(keys.reshape(B, L, self.num_key_value_heads, -1)).transpose(
+            0, 2, 1, 3
+        )
+        values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
+            0, 2, 1, 3
+        )
+
+        if cache is not None:
+            queries = self.rope(queries, offset=cache.offset)
+            keys = self.rope(keys, offset=cache.offset)
+            keys, values = cache.update_and_fetch(keys, values)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        output = scaled_dot_product_attention(
+            queries, keys, values, cache=cache, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.o_proj(output * mx.sigmoid(gate))
+
+
+class Qwen3NextMLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class Qwen3NextGatedDeltaNet(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_v_heads = config.linear_num_value_heads
+        self.num_k_heads = config.linear_num_key_heads
+        self.head_k_dim = config.linear_key_head_dim
+        self.head_v_dim = config.linear_value_head_dim
+        self.key_dim = self.head_k_dim * self.num_k_heads
+        self.value_dim = self.head_v_dim * self.num_v_heads
+        if self.num_v_heads % self.num_k_heads != 0:
+            raise ValueError(
+                f"num_v_heads ({self.num_v_heads}) must be divisible by num_k_heads ({self.num_k_heads})"
+            )
+
+        self.conv_kernel_size = config.linear_conv_kernel_dim
+        self.layer_norm_epsilon = config.rms_norm_eps
+
+        self.conv_dim = self.key_dim * 2 + self.value_dim
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=False,
+            kernel_size=self.conv_kernel_size,
+            groups=self.conv_dim,
+            padding=0,
+        )
+
+        self.in_proj_qkvz = nn.Linear(
+            self.hidden_size, self.key_dim * 2 + self.value_dim * 2, bias=False
+        )
+        self.in_proj_ba = nn.Linear(self.hidden_size, self.num_v_heads * 2, bias=False)
+
+        self.dt_bias = mx.ones(self.num_v_heads)
+
+        A = mx.random.uniform(low=0, high=16, shape=(self.num_v_heads,))
+        self.A_log = mx.log(A)
+
+        self.norm = Qwen3NextRMSNormGated(self.head_v_dim, eps=self.layer_norm_epsilon)
+
+        self.out_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
+
+    def fix_query_key_value_ordering(
+        self, mixed_qkvz: mx.array, mixed_ba: mx.array
+    ) -> mx.array:
+        nk, dn, nv, dv = (
+            self.num_k_heads,
+            self.head_k_dim,
+            self.num_v_heads,
+            self.head_v_dim,
+        )
+        mixed_qkvz = mixed_qkvz.reshape(*mixed_qkvz.shape[:-1], nk, -1)
+        mixed_ba = mixed_ba.reshape(*mixed_ba.shape[:-1], nk, -1)
+        q, k, v, z = mx.split(mixed_qkvz, [dn, 2 * dn, 2 * dn + nv // nk * dv], axis=-1)
+        b, a = mx.split(mixed_ba, [nv // nk], axis=-1)
+        return (
+            q,
+            k,
+            v.reshape(*v.shape[:2], -1, dv),
+            z.reshape(*z.shape[:2], -1, dv),
+            b.reshape(*b.shape[:2], nv),
+            a.reshape(*a.shape[:2], nv),
+        )
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, S, _ = inputs.shape
+        q, k, v, z, b, a = self.fix_query_key_value_ordering(
+            self.in_proj_qkvz(inputs), self.in_proj_ba(inputs)
+        )
+
+        if cache is not None and cache[0] is not None:
+            conv_state = cache[0]
+        else:
+            conv_state = mx.zeros(
+                (B, self.conv_kernel_size - 1, self.conv_dim),
+                dtype=inputs.dtype,
+            )
+
+        mixed_qkv = mx.concatenate(
+            [q.reshape(B, S, -1), k.reshape(B, S, -1), v.reshape(B, S, -1)], axis=-1
+        )
+        conv_input = mx.concatenate([conv_state, mixed_qkv], axis=1)
+        if cache is not None:
+            cache[0] = conv_input[:, -(self.conv_kernel_size - 1) :]
+        conv_out = nn.silu(self.conv1d(conv_input))
+
+        q, k, v = [
+            t.reshape(B, S, h, d)
+            for t, h, d in zip(
+                mx.split(conv_out, [self.key_dim, 2 * self.key_dim], -1),
+                [self.num_k_heads, self.num_k_heads, self.num_v_heads],
+                [self.head_k_dim, self.head_k_dim, self.head_v_dim],
+            )
+        ]
+
+        if cache is not None and cache[1] is not None:
+            state = cache[1]
+        else:
+            state = mx.zeros(
+                (B, self.num_v_heads, self.head_k_dim, self.head_v_dim),
+                dtype=inputs.dtype,
+            )
+
+        out, new_state = recurrent_gated_delta_rule(
+            q,
+            k,
+            v,
+            a,
+            b,
+            self.A_log,
+            self.dt_bias,
+            state,
+            use_qk_l2norm_in_kernel=True,
+        )
+
+        if cache is not None:
+            cache[1] = state
+
+        out = self.norm(out, z)
+        return self.out_proj(out.reshape(B, S, -1))
+
+
+class Qwen3NextSparseMoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        dim = args.hidden_size
+        intermediate_size = args.moe_intermediate_size
+        shared_expert_intermediate_size = args.shared_expert_intermediate_size
+
+        self.norm_topk_prob = args.norm_topk_prob
+        self.num_experts = num_experts = args.num_experts
+        self.top_k = args.num_experts_per_tok
+
+        self.gate = nn.Linear(dim, num_experts, bias=False)
+        self.switch_mlp = SwitchGLU(dim, intermediate_size, num_experts)
+
+        self.shared_expert = Qwen3NextMLP(dim, shared_expert_intermediate_size)
+        self.shared_expert_gate = nn.Linear(dim, 1, bias=False)
+
+    def __call__(
+        self,
+        x: mx.array,
+    ) -> mx.array:
+        gates = self.gate(x)
+        gates = mx.softmax(gates, axis=-1, precise=True)
+
+        k = self.top_k
+        inds = mx.argpartition(gates, kth=-k, axis=-1)[..., -k:]
+        scores = mx.take_along_axis(gates, inds, axis=-1)
+        if self.norm_topk_prob:
+            scores = scores / scores.sum(axis=-1, keepdims=True)
+
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+
+        shared_y = self.shared_expert(x)
+        shared_y = mx.sigmoid(self.shared_expert_gate(x)) * shared_y
+
+        return y + shared_y
+
+
+class Qwen3NextDecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.is_linear = (layer_idx + 1) % args.full_attention_interval != 0
+        if self.is_linear:
+            self.linear_attn = Qwen3NextGatedDeltaNet(args)
+        else:
+            self.self_attn = Qwen3NextAttention(args)
+
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        if (layer_idx not in args.mlp_only_layers) and (
+            args.num_experts > 0 and (layer_idx + 1) % args.decoder_sparse_step == 0
+        ):
+            self.mlp = Qwen3NextSparseMoeBlock(args)
+        else:
+            self.mlp = Qwen3NextMLP(args.hidden_size, args.intermediate_size)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        if self.is_linear:
+            r = self.linear_attn(self.input_layernorm(x), mask, cache)
+        else:
+            r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        out = h + self.mlp(self.post_attention_layernorm(h))
+        return out
+
+
+class Qwen3NextModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            Qwen3NextDecoderLayer(args=args, layer_idx=i)
+            for i in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.fa_idx = args.full_attention_interval - 1
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        hidden_states = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        mask = create_attention_mask(hidden_states, cache[self.fa_idx])
+
+        for layer, c in zip(self.layers, cache):
+            hidden_states = layer(hidden_states, mask=mask, cache=c)
+
+        return self.norm(hidden_states)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = Qwen3NextModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        out = self.model(inputs, cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    def make_cache(self):
+        return [MambaCache() if l.is_linear else KVCache() for l in self.layers]
+
+    def sanitize(self, weights):
+        if "model.layers.0.mlp.experts.0.up_proj.weight" not in weights:
+            return weights
+        weights = {key: value for key, value in weights.items() if "mtp." not in key}
+
+        if self.args.tie_word_embeddings:
+            weights.pop("lm_head.weight", None)
+
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}.mlp"
+            for n in ["up_proj", "down_proj", "gate_proj"]:
+                to_join = [
+                    weights.pop(f"{prefix}.experts.{e}.{n}.weight")
+                    for e in range(self.args.num_experts)
+                ]
+                weights[f"{prefix}.switch_mlp.{n}.weight"] = mx.stack(to_join)
+
+        norm_keys = (
+            ".input_layernorm.weight",
+            ".post_attention_layernorm.weight",
+            "model.norm.weight",
+            ".q_norm.weight",
+            ".k_norm.weight",
+        )
+        for k, v in weights.items():
+            if "conv1d.weight" in k and v.shape[-1] != 1:
+                weights[k] = v.moveaxis(2, 1)
+            if any(k.endswith(sfx) for sfx in norm_keys):
+                if v.ndim == 1:
+                    weights[k] = v + 1.0
+        return weights
+
+    @property
+    def quant_predicate(self):
+        def predicate(path, _):
+            if path.endswith("mlp.gate") or path.endswith("shared_expert_gate"):
+                return {"group_size": 64, "bits": 8}
+            return True
+
+        return predicate
diff --git a/mlx_lm/tuner/utils.py b/mlx_lm/tuner/utils.py
index 4b3e5d8f7..82988998a 100644
--- a/mlx_lm/tuner/utils.py
+++ b/mlx_lm/tuner/utils.py
@@ -128,13 +128,14 @@ def to_lora(layer):
         "longcat_flash",
         "seed_oss",
         "apertus",
+        "qwen3_next",
         "Klear",
         "lille-130m",
     }:
         keys = {"self_attn.q_proj", "self_attn.v_proj"}
         if model.model_type in ["mixtral", "phimoe"]:
             keys.add("block_sparse_moe.gate")
-        if model.model_type == "qwen2_moe":
+        if model.model_type in ["qwen2_moe", "qwen3_next"]:
             keys.add("mlp.gate")
             keys.add("mlp.shared_expert_gate")
         if model.model_type in ["olmoe", "qwen3_moe", "dots1", "Klear"]:
@@ -147,6 +148,15 @@ def to_lora(layer):
             keys.add("feed_forward.gate_proj")
             keys.add("feed_forward.up_proj")
             keys.add("feed_forward.down_proj")
+        elif model.model_type == "qwen3_next":
+            keys.add("linear_attn.in_proj_qkvz")
+            keys.add("linear_attn.out_proj")
+            keys.add("linear_attn.in_proj_ba")
+            keys.add("linear_attn.dt_bias")
+            keys.add("self_attn.q_proj")
+            keys.add("self_attn.k_proj")
+            keys.add("self_attn.v_proj")
+            keys.add("self_attn.o_proj")
     elif model.model_type == "gpt_bigcode":
         keys = {"attn.c_attn"}
     elif model.model_type == "gpt2":