Add Olmo3

ACKNOWLEDGMENTS.md

@@ -13,7 +13,7 @@ MLX LM was developed with contributions from the following individuals:
    THUKEG's `GLM`, `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`, Alibaba Qwen's `Qwen3Next`, and Allenai's `OLMoE`;
+   `Apertus`, Nikity's `Lille130m`, Alibaba Qwen's `Qwen3Next`, and Allenai's `OLMoE` and `OLMo3`;
    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

mlx_lm/models/olmo3.py

@@ -0,0 +1,211 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, 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, RotatingKVCache
+
+
+@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
+    max_position_embeddings: Optional[int]
+    num_key_value_heads: Optional[int]
+    attention_bias: bool
+    mlp_bias: bool
+    rope_theta: float
+    layer_types: List[str]
+    sliding_window: int
+    rope_traditional: bool = False
+    num_key_value_heads: Optional[int] = None
+    head_dim: Optional[int] = None
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+
+class Olmo3Attention(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.head_dim = args.head_dim or args.hidden_size // n_heads
+        self.layer_idx = layer_idx
+
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * self.head_dim, bias=args.attention_bias)
+        self.k_proj = nn.Linear(
+            dim, n_kv_heads * self.head_dim, bias=args.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            dim, n_kv_heads * self.head_dim, bias=args.attention_bias
+        )
+        self.o_proj = nn.Linear(n_heads * self.head_dim, dim, bias=args.attention_bias)
+
+        self.q_norm = nn.RMSNorm(dims=self.head_dim, eps=args.rms_norm_eps)
+        self.k_norm = nn.RMSNorm(dims=self.head_dim, eps=args.rms_norm_eps)
+        self.is_sliding = (layer_idx + 1) % args.layer_types[layer_idx] != 0
+
+        rope_base = args.rope_theta
+        if self.is_sliding:
+            self.rope = nn.RoPE(
+                self.head_dim, traditional=args.rope_traditional, base=rope_base
+            )
+        else:
+            self.rope = nn.RoPE(
+                self.head_dim,
+                traditional=args.rope_traditional,
+                base=rope_base,
+                scale=args.rope_scaling if hasattr(args, "rope_scaling") else 1.0,
+            )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = 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.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)
+
+        queries = self.q_norm(queries)
+        keys = self.k_norm(keys)
+
+        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)
+
+        if isinstance(mask, mx.array) and mask.shape[-1] != keys.shape[-2]:
+            mask = mask[..., -keys.shape[-2] :]
+        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 Olmo3MLP(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.gate_proj = nn.Linear(
+            args.hidden_size, args.intermediate_size, bias=args.mlp_bias
+        )
+        self.down_proj = nn.Linear(
+            args.intermediate_size, args.hidden_size, bias=args.mlp_bias
+        )
+        self.up_proj = nn.Linear(
+            args.hidden_size, args.intermediate_size, bias=args.mlp_bias
+        )
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class Olmo3DecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Olmo3Attention(args)
+        self.mlp = Olmo3MLP(args)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        self.post_feedforward_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        r = self.post_attention_layernorm(self.self_attn(x, mask, cache))
+        h = x + r
+        r = self.post_feedforward_layernorm(self.mlp(h))
+        out = h + r
+        return out
+
+
+class Olmo3Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            Olmo3DecoderLayer(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,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        h = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        mask = create_attention_mask(h, cache[0])
+
+        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 = Olmo3Model(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
+
+    def sanitize(self, weights):
+        return {
+            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
+        }
+
+    @property
+    def layers(self):
+        return self.model.layers

mlx_lm/tuner/utils.py

@@ -133,6 +133,7 @@ def to_lora(layer):
         "qwen3_next",
         "Klear",
         "lille-130m",
+        "olmo3",
     }:
         keys = {"self_attn.q_proj", "self_attn.v_proj"}
         if model.model_type in ["mixtral", "phimoe"]: