diff --git a/llms/mlx_lm/models/internlm3.py b/llms/mlx_lm/models/internlm3.py
new file mode 100644
index 00000000..3be6f536
--- /dev/null
+++ b/llms/mlx_lm/models/internlm3.py
@@ -0,0 +1,241 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    rms_norm_eps: float
+    vocab_size: int
+    bias: bool = False
+    qkv_bias: bool = False
+    max_position_embeddings: int = 32768
+    num_key_value_heads: int = None
+    rope_theta: float = 10000
+    rope_traditional: bool = False
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        if self.rope_scaling:
+            required_keys = {"factor", "rope_type"}
+            if not all(key in self.rope_scaling for key in required_keys):
+                raise ValueError(f"rope_scaling must contain keys {required_keys}")
+
+            if self.rope_scaling["rope_type"] not in ["linear", "dynamic"]:
+                raise ValueError(
+                    "rope_scaling 'rope_type' currently only supports 'linear' or 'dynamic"
+                )
+
+
+class DynamicNTKScalingRoPE(nn.Module):
+    """Implements the rotary positional encoding with Dynamic NTK scaling."""
+
+    def __init__(
+        self,
+        dims: int,
+        max_position_embeddings: int = 2048,
+        traditional: bool = False,
+        base: float = 10000,
+        scale: float = 1.0,
+    ):
+        super().__init__()
+        self.max_position_embeddings = max_position_embeddings
+        self.original_base = base
+        self.dims = dims
+        self.traditional = traditional
+        self.scale = scale
+
+    def extra_repr(self):
+        return f"{self.dims}, traditional={self.traditional}, max_position_embeddings={self.max_position_embeddings}, scaling_factor={self.scaling_factor}"
+
+    def __call__(self, x, offset: int = 0):
+        seq_len = x.shape[1] + offset
+        if seq_len > self.max_position_embeddings:
+            base = self.original_base * (
+                (self.scale * seq_len / self.max_position_embeddings) - (self.scale - 1)
+            ) ** (self.dims / (self.dims - 2))
+        else:
+            base = self.original_base
+
+        return mx.fast.rope(
+            x,
+            self.dims,
+            traditional=self.traditional,
+            base=base,
+            scale=self.scale,
+            offset=offset,
+        )
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        qkv_bias = args.qkv_bias
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.n_kv_groups = n_heads // args.num_key_value_heads
+
+        self.head_dim = head_dim = args.hidden_size // n_heads
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=qkv_bias)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=qkv_bias)
+
+        rope_scale = (
+            1 / args.rope_scaling["factor"]
+            if args.rope_scaling is not None
+            and args.rope_scaling["rope_type"] == "linear"
+            else 2.0
+        )
+
+        self.rope = DynamicNTKScalingRoPE(
+            head_dim,
+            max_position_embeddings=args.max_position_embeddings,
+            traditional=args.rope_traditional,
+            base=args.rope_theta,
+            scale=rope_scale,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_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 = 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)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim, bias):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=bias)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=bias)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=bias)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size, args.bias)
+        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
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = 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 InternLM2Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        assert args.vocab_size > 0
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache=None,
+    ):
+        h = self.embed_tokens(inputs)
+
+        if mask is None:
+            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, cache=c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = InternLM2Model(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,
+        mask: mx.array = None,
+        cache=None,
+    ):
+        out = self.model(inputs, mask, cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    def sanitize(self, weights):
+        # Remove unused precomputed rotary freqs
+        return {k: v for k, v in weights.items() if "attention.rope.inv_freq" not in k}
+
+    @property
+    def layers(self):
+        return self.model.layers
diff --git a/llms/mlx_lm/tuner/utils.py b/llms/mlx_lm/tuner/utils.py
index 3986952a..594f8040 100644
--- a/llms/mlx_lm/tuner/utils.py
+++ b/llms/mlx_lm/tuner/utils.py
@@ -100,6 +100,7 @@ def linear_to_lora_layers(
         "minicpm",
         "deepseek",
         "olmo2",
+        "internlm3",
     ]:
         keys = set(["self_attn.q_proj", "self_attn.v_proj"])
         if model.model_type in ["mixtral", "phimoe"]:
diff --git a/llms/tests/test_models.py b/llms/tests/test_models.py
index 118ec6f2..d8cf6820 100644
--- a/llms/tests/test_models.py
+++ b/llms/tests/test_models.py
@@ -927,6 +927,23 @@ class TestModels(unittest.TestCase):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_internlm3(self):
+        from mlx_lm.models import internlm3
+
+        args = internlm3.ModelArgs(
+            model_type="internlm3",
+            hidden_size=1024,
+            num_hidden_layers=4,
+            intermediate_size=2048,
+            num_attention_heads=4,
+            rms_norm_eps=1e-5,
+            vocab_size=10_000,
+        )
+        model = internlm3.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
 
 if __name__ == "__main__":
     unittest.main()