diff --git a/lib/bumblebee.ex b/lib/bumblebee.ex
index 13928ee1..7a78d7df 100644
--- a/lib/bumblebee.ex
+++ b/lib/bumblebee.ex
@@ -176,6 +176,7 @@ defmodule Bumblebee do
     "MPNetForTokenClassification" => {Bumblebee.Text.MpNet, :for_token_classification},
     "MPNetForQuestionAnswering" => {Bumblebee.Text.MpNet, :for_question_answering},
     "MPNetForMultipleChoice" => {Bumblebee.Text.MpNet, :for_multiple_choice},
+    "NomicBertModel" => {Bumblebee.Text.NomicBert, :base},
     "PhiModel" => {Bumblebee.Text.Phi, :base},
     "PhiForCausalLM" => {Bumblebee.Text.Phi, :for_causal_language_modeling},
     "PhiForSequenceClassification" => {Bumblebee.Text.Phi, :for_sequence_classification},
@@ -266,6 +267,7 @@ defmodule Bumblebee do
     "mistral" => :llama,
     "mbart" => :mbart,
     "mpnet" => :mpnet,
+    "nomic_bert" => :bert,
     "phi" => :code_gen,
     "phi3" => :llama,
     "qwen3" => :qwen2,
diff --git a/lib/bumblebee/text/nomic_bert.ex b/lib/bumblebee/text/nomic_bert.ex
new file mode 100644
index 00000000..98a4ef84
--- /dev/null
+++ b/lib/bumblebee/text/nomic_bert.ex
@@ -0,0 +1,357 @@
+defmodule Bumblebee.Text.NomicBert do
+  alias Bumblebee.Shared
+
+  options =
+    [
+      vocab_size: [
+        default: 30528,
+        doc: """
+        the vocabulary size of the token embedding. This corresponds to the number of distinct
+        tokens that can be represented in model input and output
+        """
+      ],
+      max_positions: [
+        default: 8192,
+        doc: """
+        the maximum sequence length that this model can process. Typically this is set to a large
+        value just in case, such as 512, 1024 or 2048
+        """
+      ],
+      max_token_types: [
+        default: 2,
+        doc: """
+        the vocabulary size of the token type embedding (also referred to as segment embedding).
+        This corresponds to how many different token groups can be distinguished in the input
+        """
+      ],
+      hidden_size: [
+        default: 768,
+        doc: "the dimensionality of hidden layers"
+      ],
+      num_blocks: [
+        default: 12,
+        doc: "the number of Transformer blocks in the encoder"
+      ],
+      num_attention_heads: [
+        default: 12,
+        doc: "the number of attention heads for each attention layer in the encoder"
+      ],
+      intermediate_size: [
+        default: nil,
+        doc:
+          "the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the encoder. Defaults to 4 * hidden_size"
+      ],
+      activation: [
+        default: :silu,
+        doc: "the activation function"
+      ],
+      rotary_embedding_base: [
+        default: 1000,
+        doc: "base for computing rotary embedding frequency"
+      ],
+      rotary_embedding_percentage: [
+        default: 1.0,
+        doc: "percentage of hidden size to use for rotary embeddings"
+      ],
+      layer_norm_epsilon: [
+        default: 1.0e-5,
+        doc: "the epsilon used by the layer normalization layers"
+      ],
+      initializer_scale: [
+        default: 0.02,
+        doc:
+          "the standard deviation of the normal initializer used for initializing kernel parameters"
+      ],
+      ffn_gate_bias: [
+        default: true,
+        doc: "whether to use bias in the up and gate projections of the FFN"
+      ],
+      ffn_output_bias: [
+        default: true,
+        doc: "whether to use bias in the output projection of the FFN"
+      ]
+    ] ++ Shared.common_options([:num_labels, :id_to_label])
+
+  @moduledoc """
+  Nomic BERT model family.
+
+  This is a variant of BERT that uses:
+  - Rotary position embeddings (RoPE) instead of absolute position embeddings
+  - SwiGLU activation in the feed-forward network
+  - Post-normalization (like original BERT)
+  - No biases in attention and feed-forward layers
+
+  ## Architectures
+
+    * `:base` - plain Nomic BERT without any head on top
+
+  ## Inputs
+
+    * `"input_ids"` - `{batch_size, sequence_length}`
+
+      Indices of input sequence tokens in the vocabulary.
+
+    * `"attention_mask"` - `{batch_size, sequence_length}`
+
+      Mask indicating which tokens to attend to. This is used to ignore
+      padding tokens, which are added when processing a batch of sequences
+      with different length.
+
+    * `"position_ids"` - `{batch_size, sequence_length}`
+
+      Indices of positions of each input sequence token used when applying
+      rotary position embeddings (RoPE).
+
+  ## Global layer options
+
+  #{Shared.global_layer_options_doc([:output_hidden_states, :output_attentions])}
+
+  ## Configuration
+
+  #{Shared.options_doc(options)}
+  """
+
+  defstruct [architecture: :base] ++ Shared.option_defaults(options)
+
+  @behaviour Bumblebee.ModelSpec
+  @behaviour Bumblebee.Configurable
+
+  import Bumblebee.Utils.Model, only: [join: 2]
+
+  alias Bumblebee.Layers
+
+  @impl true
+  def architectures(), do: [:base]
+
+  @impl true
+  def config(spec, opts) do
+    spec
+    |> Shared.put_config_attrs(opts)
+    |> Shared.validate_label_options()
+  end
+
+  @impl true
+  def input_template(_spec) do
+    %{"input_ids" => Nx.template({1, 1}, :u32)}
+  end
+
+  @impl true
+  def model(%__MODULE__{architecture: :base} = spec) do
+    inputs = inputs(spec)
+
+    inputs
+    |> core(spec)
+    |> Layers.output()
+  end
+
+  defp inputs(spec) do
+    shape = {nil, nil}
+    attention_head_mask_shape = {spec.num_blocks, spec.num_attention_heads}
+
+    Bumblebee.Utils.Model.inputs_to_map([
+      Axon.input("input_ids", shape: shape),
+      Axon.input("attention_mask", optional: true, shape: shape),
+      Axon.input("token_type_ids", optional: true, shape: shape),
+      Axon.input("position_ids", optional: true, shape: shape),
+      Axon.input("attention_head_mask", optional: true, shape: attention_head_mask_shape)
+    ])
+  end
+
+  defp core(inputs, spec) do
+    token_type_ids =
+      Layers.default inputs["token_type_ids"] do
+        Layers.default_token_type_ids(inputs["input_ids"])
+      end
+
+    embeddings = embedder(inputs["input_ids"], token_type_ids, spec, name: "embedder")
+
+    position_ids =
+      Layers.default inputs["position_ids"] do
+        Layers.default_position_ids(embeddings)
+      end
+
+    encoder_outputs =
+      encoder(
+        embeddings,
+        position_ids,
+        inputs["attention_mask"],
+        inputs["attention_head_mask"],
+        spec,
+        name: "encoder"
+      )
+
+    pooled_state = pooler(encoder_outputs.hidden_state, spec, name: "pooler")
+
+    %{
+      hidden_state: encoder_outputs.hidden_state,
+      pooled_state: pooled_state,
+      hidden_states: encoder_outputs.hidden_states,
+      attentions: encoder_outputs.attentions
+    }
+  end
+
+  defp embedder(input_ids, token_type_ids, spec, opts) do
+    name = opts[:name]
+
+    token_embeddings =
+      Axon.embedding(input_ids, spec.vocab_size, spec.hidden_size,
+        kernel_initializer: kernel_initializer(spec),
+        name: join(name, "token_embedding")
+      )
+
+    token_type_embeddings =
+      Axon.embedding(token_type_ids, spec.max_token_types, spec.hidden_size,
+        kernel_initializer: kernel_initializer(spec),
+        name: join(name, "token_type_embedding")
+      )
+
+    Axon.add([token_embeddings, token_type_embeddings])
+    |> Axon.layer_norm(epsilon: spec.layer_norm_epsilon, name: join(name, "norm"))
+  end
+
+  defp encoder(hidden_state, position_ids, attention_mask, attention_head_mask, spec, opts) do
+    name = opts[:name]
+
+    Layers.Transformer.blocks(hidden_state,
+      attention_mask: attention_mask,
+      attention_head_mask: attention_head_mask,
+      num_blocks: spec.num_blocks,
+      num_attention_heads: spec.num_attention_heads,
+      hidden_size: spec.hidden_size,
+      kernel_initializer: kernel_initializer(spec),
+      layer_norm: [epsilon: spec.layer_norm_epsilon],
+      ffn:
+        &gated_ffn(&1, intermediate_size(spec), spec.hidden_size,
+          name: &2,
+          activation: spec.activation,
+          gate_use_bias: spec.ffn_gate_bias,
+          output_use_bias: spec.ffn_output_bias
+        ),
+      block_type: :standard,
+      causal: false,
+      rotary_embedding: [
+        position_ids: position_ids,
+        max_positions: spec.max_positions,
+        base: spec.rotary_embedding_base,
+        percentage: spec.rotary_embedding_percentage
+      ],
+      query_use_bias: false,
+      key_use_bias: false,
+      value_use_bias: false,
+      output_use_bias: false,
+      name: join(name, "blocks")
+    )
+  end
+
+  defp pooler(hidden_state, spec, opts) do
+    name = opts[:name]
+
+    hidden_state
+    |> Layers.take_token(index: 0, axis: 1)
+    |> Axon.dense(spec.hidden_size,
+      kernel_initializer: kernel_initializer(spec),
+      name: join(name, "output")
+    )
+    |> Axon.tanh()
+  end
+
+  defp gated_ffn(hidden_state, intermediate_size, output_size, opts) do
+    name = opts[:name]
+    activation = opts[:activation]
+    gate_use_bias = opts[:gate_use_bias]
+    output_use_bias = opts[:output_use_bias]
+
+    # Nomic MLP: y = fc11(x) * activation(fc12(x)), then fc2
+    # fc11 is "up", fc12 is "gate", fc2 is "down"
+    up =
+      Axon.dense(hidden_state, intermediate_size,
+        name: join(name, "up"),
+        use_bias: gate_use_bias
+      )
+
+    gate =
+      Axon.dense(hidden_state, intermediate_size,
+        name: join(name, "gate"),
+        use_bias: gate_use_bias
+      )
+
+    # Nomic applies activation to gate, not up: up * activation(gate)
+    hidden_state = Axon.multiply(up, Axon.activation(gate, activation))
+
+    Axon.dense(hidden_state, output_size, name: join(name, "down"), use_bias: output_use_bias)
+  end
+
+  defp kernel_initializer(spec) do
+    Axon.Initializers.normal(scale: spec.initializer_scale)
+  end
+
+  # NomicBERT rounds intermediate_size to nearest multiple of 256 for hardware efficiency
+  defp intermediate_size(spec) do
+    size = spec.intermediate_size || div(8 * spec.hidden_size, 3)
+    multiple_of = 256
+    div(size + multiple_of - 1, multiple_of) * multiple_of
+  end
+
+  defimpl Bumblebee.HuggingFace.Transformers.Config do
+    def load(spec, data) do
+      import Shared.Converters
+
+      opts =
+        convert!(data,
+          vocab_size: {"vocab_size", number()},
+          max_positions: {"n_positions", number()},
+          max_token_types: {"type_vocab_size", number()},
+          hidden_size: {"n_embd", number()},
+          num_blocks: {"n_layer", number()},
+          num_attention_heads: {"n_head", number()},
+          intermediate_size: {"n_inner", optional(number())},
+          rotary_embedding_base: {"rotary_emb_base", number()},
+          rotary_embedding_percentage: {"rotary_emb_fraction", optional(number())},
+          layer_norm_epsilon: {"layer_norm_epsilon", number()},
+          initializer_scale: {"initializer_range", number()},
+          ffn_gate_bias: {"mlp_fc1_bias", boolean()},
+          ffn_output_bias: {"mlp_fc2_bias", boolean()}
+        ) ++ Shared.common_options_from_transformers(data, spec)
+
+      @for.config(spec, opts)
+    end
+  end
+
+  defimpl Bumblebee.HuggingFace.Transformers.Model do
+    def params_mapping(_spec) do
+      %{
+        "embedder.token_embedding" => "embeddings.word_embeddings",
+        "embedder.token_type_embedding" => "embeddings.token_type_embeddings",
+        "embedder.norm" => "emb_ln",
+        "encoder.blocks.{n}.self_attention.query" => qkv_dense("encoder.layers.{n}.attn.Wqkv", 0),
+        "encoder.blocks.{n}.self_attention.key" => qkv_dense("encoder.layers.{n}.attn.Wqkv", 1),
+        "encoder.blocks.{n}.self_attention.value" => qkv_dense("encoder.layers.{n}.attn.Wqkv", 2),
+        "encoder.blocks.{n}.self_attention.output" => "encoder.layers.{n}.attn.out_proj",
+        "encoder.blocks.{n}.self_attention_norm" => "encoder.layers.{n}.norm1",
+        "encoder.blocks.{n}.ffn.up" => "encoder.layers.{n}.mlp.fc11",
+        "encoder.blocks.{n}.ffn.gate" => "encoder.layers.{n}.mlp.fc12",
+        "encoder.blocks.{n}.ffn.down" => "encoder.layers.{n}.mlp.fc2",
+        "encoder.blocks.{n}.output_norm" => "encoder.layers.{n}.norm2",
+        "pooler.output" => "pooler.dense"
+      }
+    end
+
+    defp qkv_dense(source_layer_name, chunk_idx) do
+      # Wqkv is [3 * hidden_size, hidden_size] in PyTorch format
+      # After slicing, transpose to get [hidden_size, hidden_size] for Axon
+      %{
+        "kernel" => {
+          [{source_layer_name, "weight"}],
+          fn [kernel] ->
+            size = Nx.axis_size(kernel, 0)
+            step = div(size, 3)
+
+            kernel
+            |> Nx.slice_along_axis(chunk_idx * step, step, axis: 0)
+            |> Nx.transpose()
+          end
+        }
+      }
+    end
+  end
+end
diff --git a/mix.exs b/mix.exs
index 47b2f90f..089c7f9d 100644
--- a/mix.exs
+++ b/mix.exs
@@ -101,6 +101,7 @@ defmodule Bumblebee.MixProject do
           Bumblebee.Text.Mbart,
           Bumblebee.Text.Mistral,
           Bumblebee.Text.MpNet,
+          Bumblebee.Text.NomicBert,
           Bumblebee.Text.Phi,
           Bumblebee.Text.Phi3,
           Bumblebee.Text.Roberta,
diff --git a/test/bumblebee/text/nomic_bert_test.exs b/test/bumblebee/text/nomic_bert_test.exs
new file mode 100644
index 00000000..92b1829b
--- /dev/null
+++ b/test/bumblebee/text/nomic_bert_test.exs
@@ -0,0 +1,36 @@
+defmodule Bumblebee.Text.NomicBertTest do
+  use ExUnit.Case, async: true
+
+  import Bumblebee.TestHelpers
+
+  @moduletag model_test_tags()
+
+  test ":base" do
+    assert {:ok, %{model: model, params: params, spec: spec}} =
+             Bumblebee.load_model({:hf, "bumblebee-testing/tiny-random-NomicBertModel"})
+
+    assert %Bumblebee.Text.NomicBert{architecture: :base} = spec
+
+    inputs = %{
+      "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]),
+      "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.hidden_state) == {1, 10, 32}
+    assert Nx.shape(outputs.pooled_state) == {1, 32}
+
+    assert_all_close(
+      outputs.hidden_state[[.., 1..3, 1..3]],
+      Nx.tensor([
+        [[0.3062, -2.1607, -0.1782], [0.5486, 0.5353, -0.0453], [0.6323, -0.7370, -2.0245]]
+      ])
+    )
+
+    assert_all_close(
+      outputs.pooled_state[[.., 1..3]],
+      Nx.tensor([[0.0197, -0.2129, -0.0071]])
+    )
+  end
+end