diff --git a/llms/mlx_lm/models/deepseek.py b/llms/mlx_lm/models/deepseek.py
new file mode 100644
index 00000000..dcfa331c
--- /dev/null
+++ b/llms/mlx_lm/models/deepseek.py
@@ -0,0 +1,266 @@
+from dataclasses import dataclass
+from typing import Dict, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, KVCache, create_attention_mask
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "deepseek"
+    vocab_size: int = 102400
+    hidden_size: int = 4096
+    intermediate_size: int = 11008
+    moe_intermediate_size: int = 1407
+    num_hidden_layers: int = 30
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 32
+    n_shared_experts: Optional[int] = None
+    n_routed_experts: Optional[int] = None
+    num_experts_per_tok: Optional[int] = None
+    moe_layer_freq: int = 1
+    first_k_dense_replace: int = 0
+    max_position_embeddings: int = 2048
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000.0
+    rope_scaling: Optional[Dict] = None
+    attention_bias: bool = False
+
+
+class DeepseekAttention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.scale = self.head_dim**-0.5
+
+        attention_bias = getattr(config, "attention_bias", False)
+
+        self.q_proj = nn.Linear(
+            self.hidden_size,
+            config.num_attention_heads * self.head_dim,
+            bias=attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            self.hidden_size,
+            config.num_attention_heads * self.head_dim,
+            bias=attention_bias,
+        )
+
+        rope_scale = 1.0
+        if config.rope_scaling and config.rope_scaling["type"] == "linear":
+            assert isinstance(config.rope_scaling["factor"], float)
+            rope_scale = 1 / config.rope_scaling["factor"]
+        self.rope = nn.RoPE(
+            self.head_dim,
+            base=config.rope_theta,
+            scale=rope_scale,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        B, L, _ = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        queries = queries.reshape(B, L, self.num_attention_heads, -1).transpose(
+            0, 2, 1, 3
+        )
+        keys = keys.reshape(B, L, self.num_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.num_kv_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 = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class DeepseekMLP(nn.Module):
+    def __init__(
+        self,
+        config: ModelArgs,
+        hidden_size: int | None = None,
+        intermediate_size: int | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size or config.hidden_size
+        self.intermediate_size = intermediate_size or config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = nn.silu
+
+    def __call__(self, x: mx.array) -> mx.array:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
+
+    def __call__(self, x):
+        gates = x @ self.weight.T
+        scores = mx.softmax(gates, axis=-1, precise=True)
+        k = self.top_k
+        inds = mx.stop_gradient(mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k])
+        scores = mx.take_along_axis(scores, inds, axis=-1)
+        return inds, scores
+
+
+class DeepseekMoE(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.switch_mlp = SwitchGLU(
+            config.hidden_size, config.moe_intermediate_size, config.n_routed_experts
+        )
+
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekMLP(
+                config=config, intermediate_size=intermediate_size
+            )
+
+    def __call__(self, x):
+        inds, scores = self.gate(x)
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(x)
+
+        return y
+
+
+class DeepseekDecoderLayer(nn.Module):
+    def __init__(self, config: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.self_attn = DeepseekAttention(config)
+        self.mlp = (
+            DeepseekMoE(config)
+            if (
+                config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0
+            )
+            else DeepseekMLP(config)
+        )
+        self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out
+
+
+class DeepseekModel(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = [
+            DeepseekDecoderLayer(config, idx) for idx in range(config.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def __call__(
+        self,
+        x: mx.array,
+        cache: Optional[KVCache] = None,
+    ) -> mx.array:
+        h = self.embed_tokens(x)
+        mask = create_attention_mask(h, cache)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for layer, c in zip(self.layers, cache):
+            h = layer(h, mask, c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.args = config
+        self.model_type = config.model_type
+        self.model = DeepseekModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[KVCache] = None,
+    ):
+        out = self.model(inputs, cache)
+        return self.lm_head(out)
+
+    def sanitize(self, weights):
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}"
+            for m in ["gate_proj", "down_proj", "up_proj"]:
+                for k in ["weight", "scales", "biases"]:
+                    if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
+                        to_join = [
+                            weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
+                            for e in range(self.args.n_routed_experts)
+                        ]
+                        weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def head_dim(self):
+        return self.args.hidden_size // self.args.num_attention_heads
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads
diff --git a/llms/mlx_lm/tuner/utils.py b/llms/mlx_lm/tuner/utils.py
index b7f1f9de..c6af9730 100644
--- a/llms/mlx_lm/tuner/utils.py
+++ b/llms/mlx_lm/tuner/utils.py
@@ -101,6 +101,7 @@ def linear_to_lora_layers(
         "starcoder2",
         "cohere",
         "minicpm",
+        "deepseek",
     ]:
         keys = set(["self_attn.q_proj", "self_attn.v_proj"])
         if model.model_type == "mixtral":