diff --git a/examples/gemma3.py b/examples/gemma3.py
new file mode 100644
index 00000000..e337c6d1
--- /dev/null
+++ b/examples/gemma3.py
@@ -0,0 +1,41 @@
+import heapq
+from embed_anything import EmbedData, EmbeddingModel, TextEmbedConfig, WhichModel, Dtype
+
+from embed_anything import Dtype, ONNXModel
+import numpy as np
+import os
+from huggingface_hub import login
+
+
+model: EmbeddingModel = EmbeddingModel.from_pretrained_hf(
+    model_id="google/embeddinggemma-300m", dtype=Dtype.F32, token="hf_key"
+)
+
+config = TextEmbedConfig(
+    chunk_size=1000,
+    batch_size=2,
+    splitting_strategy="sentence",
+    late_chunking=False,
+)
+
+# Embed a single file
+data: list[EmbedData] = model.embed_file("test_files/attention.pdf", config=config)
+
+
+query = "Which GPU is used for training"
+
+query_embedding = np.array(model.embed_query([query])[0].embedding)
+
+embedding_array = np.array([e.embedding for e in data])
+
+similarities = np.matmul(query_embedding, embedding_array.T)
+
+# get top 5 similarities and its index
+top_5_similarities = np.argsort(similarities)[-10:][::-1]
+
+# Print the top 5 similarities with sentences
+for i in top_5_similarities:
+    print(f"Score: {similarities[i]:.2} | {data[i].text}")
+    print("---" * 20)
+
+context = "\n".join([data[i].text for i in top_5_similarities])
\ No newline at end of file
diff --git a/rust/src/embeddings/embed/embedder.rs b/rust/src/embeddings/embed/embedder.rs
index fdcbc1e5..28e525c1 100644
--- a/rust/src/embeddings/embed/embedder.rs
+++ b/rust/src/embeddings/embed/embedder.rs
@@ -114,6 +114,13 @@ impl Embedder {
                 token,
                 dtype,
             )?)),
+            "Gemma3TextModel" => Ok(Self::Text(TextEmbedder::from_pretrained_hf(
+                architecture,
+                model_id,
+                revision,
+                token,
+                dtype,
+            )?)),
             _ => Err(anyhow!("Model not supported")),
         }
     }
diff --git a/rust/src/embeddings/embed/text.rs b/rust/src/embeddings/embed/text.rs
index 354be4f2..8779d55b 100644
--- a/rust/src/embeddings/embed/text.rs
+++ b/rust/src/embeddings/embed/text.rs
@@ -2,6 +2,7 @@ use crate::embeddings::cloud::cohere::CohereEmbedder;
 use crate::embeddings::cloud::gemini::GeminiEmbedder;
 use crate::embeddings::cloud::openai::OpenAIEmbedder;
 use crate::embeddings::local::bert::{BertEmbed, BertEmbedder, SparseBertEmbedder};
+use crate::embeddings::local::gemma3::{Gemma3Embed, Gemma3Embedder};
 use crate::embeddings::local::jina::{JinaEmbed, JinaEmbedder};
 use crate::embeddings::local::model2vec::Model2VecEmbedder;
 use crate::embeddings::local::modernbert::ModernBertEmbedder;
@@ -29,6 +30,7 @@ pub enum TextEmbedder {
     Model2Vec(Box<Model2VecEmbedder>),
     Bert(Box<dyn BertEmbed + Send + Sync>),
     Qwen3(Box<dyn Qwen3Embed + Send + Sync>),
+    Gemma3(Box<dyn Gemma3Embed + Send + Sync>),
     ColBert(Box<dyn BertEmbed + Send + Sync>),
     ModernBert(Box<dyn BertEmbed + Send + Sync>),
 }
@@ -48,6 +50,7 @@ impl TextEmbedder {
             TextEmbedder::Jina(embedder) => embedder.embed(text_batch, batch_size, late_chunking),
             TextEmbedder::Bert(embedder) => embedder.embed(text_batch, batch_size, late_chunking),
             TextEmbedder::Qwen3(embedder) => embedder.embed(text_batch, batch_size, late_chunking),
+            TextEmbedder::Gemma3(embedder) => embedder.embed(text_batch, batch_size, late_chunking),
             TextEmbedder::ColBert(embedder) => {
                 embedder.embed(text_batch, batch_size, late_chunking)
             }
@@ -95,6 +98,12 @@ impl TextEmbedder {
                 token,
                 dtype,
             )?))),
+            "Gemma3TextModel" => Ok(Self::Gemma3(Box::new(Gemma3Embedder::new(
+                model_id,
+                revision.map(|s| s.to_string()),
+                token,
+                dtype,
+            )?))),
             _ => Err(anyhow!("Model not supported")),
         }
     }
diff --git a/rust/src/embeddings/local/gemma3.rs b/rust/src/embeddings/local/gemma3.rs
new file mode 100644
index 00000000..917e8ac3
--- /dev/null
+++ b/rust/src/embeddings/local/gemma3.rs
@@ -0,0 +1,241 @@
+use crate::{
+    embeddings::{embed::EmbeddingResult, normalize_l2, select_device, utils::tokenize_batch},
+    models::gemma3::{Config, Model},
+};
+use anyhow::Error;
+use candle_core::{DType, Device, Tensor};
+use candle_nn::{Linear, Module, VarBuilder};
+use hf_hub::{api::sync::ApiBuilder, Repo};
+use tokenizers::{PaddingParams, Tokenizer, TruncationParams};
+
+use super::{
+    colpali::hub_load_safetensors,
+    pooling::{ModelOutput, PooledOutputType, Pooling},
+};
+
+pub trait Gemma3Embed {
+    fn embed(
+        &self,
+        text_batch: &[&str],
+        batch_size: Option<usize>,
+        late_chunking: Option<bool>,
+    ) -> Result<Vec<EmbeddingResult>, anyhow::Error>;
+}
+
+pub struct Gemma3Embedder {
+    pub model: std::sync::RwLock<Model>,
+    pub tokenizer: Tokenizer,
+    pub device: Device,
+    dense1: Linear,
+    dense2: Linear,
+}
+
+/// Loads a sentence-transformers `Dense` module (a single no-bias `nn.Linear`
+/// stored under the key `linear.weight`) from a safetensors file in the repo.
+fn load_dense(
+    repo: &hf_hub::api::sync::ApiRepo,
+    path: &str,
+    device: &Device,
+) -> Result<Linear, anyhow::Error> {
+    let weights_path = repo.get(path)?;
+    let mut tensors = candle_core::safetensors::load(weights_path, device)?;
+    let weight = tensors
+        .remove("linear.weight")
+        .ok_or_else(|| anyhow::anyhow!("missing 'linear.weight' in {path}"))?
+        .to_dtype(DType::F32)?;
+    Ok(Linear::new(weight, None))
+}
+
+impl Gemma3Embedder {
+    pub fn new(
+        model_id: &str,
+        revision: Option<String>,
+        token: Option<&str>,
+        dtype: Option<crate::Dtype>,
+    ) -> Result<Self, anyhow::Error> {
+        let mut api_builder = ApiBuilder::from_env();
+        if let Some(token) = token {
+            api_builder = api_builder.with_token(Some(token.to_string()));
+        }
+        let api = api_builder.build()?;
+
+        let repo = match revision {
+            Some(rev) => api.repo(Repo::with_revision(
+                model_id.to_string(),
+                hf_hub::RepoType::Model,
+                rev,
+            )),
+            None => api.repo(hf_hub::Repo::new(
+                model_id.to_string(),
+                hf_hub::RepoType::Model,
+            )),
+        };
+        let (config_filename, tokenizer_filename, weights_filename) = {
+            let config = repo.get("config.json")?;
+            let tokenizer = repo.get("tokenizer.json")?;
+            let weights = repo.get("model.safetensors");
+
+            (config, tokenizer, weights)
+        };
+
+        let config = std::fs::read_to_string(config_filename)?;
+        let config: Config = serde_json::from_str(&config)?;
+
+        let mut tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(Error::msg)?;
+        let pp = PaddingParams {
+            strategy: tokenizers::PaddingStrategy::BatchLongest,
+            direction: tokenizers::PaddingDirection::Left,
+            ..Default::default()
+        };
+        let trunc = TruncationParams {
+            strategy: tokenizers::TruncationStrategy::LongestFirst,
+            max_length: 1024,
+            ..Default::default()
+        };
+
+        tokenizer
+            .with_padding(Some(pp))
+            .with_truncation(Some(trunc))
+            .map_err(Error::msg)?;
+
+        let device = select_device();
+
+        let dtype = match dtype {
+            Some(crate::Dtype::F16) => DType::F16,
+            Some(crate::Dtype::F32) => DType::F32,
+            Some(crate::Dtype::BF16) => DType::BF16,
+            _ => DType::F32,
+        };
+
+        let vb = match weights_filename {
+            Ok(weights) => unsafe {
+                VarBuilder::from_mmaped_safetensors(&[weights], dtype, &device)?
+            },
+            Err(_) => {
+                let weights = hub_load_safetensors(&repo, "model.safetensors.index.json")?;
+                unsafe { VarBuilder::from_mmaped_safetensors(&weights, dtype, &device)? }
+            }
+        };
+
+        let model = Model::new(false, &config, vb)?; // use_flash_attn = false by default
+
+        // EmbeddingGemma applies a 768 -> 3072 -> 768 dense projection head
+        // (sentence-transformers modules 2_Dense / 3_Dense, both no-bias with
+        // Identity activation) between mean pooling and L2 normalization. These
+        // weights live outside model.safetensors and must be applied separately.
+        let dense1 = load_dense(&repo, "2_Dense/model.safetensors", &device)?;
+        let dense2 = load_dense(&repo, "3_Dense/model.safetensors", &device)?;
+
+        Ok(Self {
+            model: std::sync::RwLock::new(model),
+            tokenizer,
+            device,
+            dense1,
+            dense2,
+        })
+    }
+}
+
+impl Gemma3Embed for Gemma3Embedder {
+    fn embed(
+        &self,
+        text_batch: &[&str],
+        batch_size: Option<usize>,
+        _late_chunking: Option<bool>,
+    ) -> Result<Vec<EmbeddingResult>, anyhow::Error> {
+        let batch_size = batch_size.unwrap_or(32);
+        let mut encodings: Vec<EmbeddingResult> = Vec::new();
+
+        for mini_text_batch in text_batch.chunks(batch_size) {
+            let (token_ids, attention_mask) =
+                tokenize_batch(&self.tokenizer, mini_text_batch, &self.device)?;
+
+            // Forward pass through the model. EmbeddingGemma uses bidirectional
+            // attention, so the padding mask must be passed in (not just used for pooling).
+            let embeddings: Tensor = self
+                .model
+                .write()
+                .unwrap()
+                .forward(&token_ids, Some(&attention_mask), 0)
+                .unwrap()
+                .to_dtype(DType::F32)?;
+
+            self.model.write().unwrap().clear_kv_cache();
+
+            // Convert attention_mask to the expected format for pooling
+            let attention_mask = PooledOutputType::from(attention_mask);
+            let attention_mask = Some(&attention_mask);
+            let model_output = ModelOutput::Tensor(embeddings.clone());
+            let pooled_output = Pooling::Mean.pool(&model_output, attention_mask).unwrap();
+            let pooled_output = pooled_output.to_tensor()?;
+            let projected = self.dense2.forward(&self.dense1.forward(pooled_output)?)?;
+            let embeddings = normalize_l2(&projected)?;
+            let batch_encodings = embeddings.to_vec2::<f32>()?;
+
+            encodings.extend(
+                batch_encodings
+                    .iter()
+                    .map(|x| EmbeddingResult::DenseVector(x.to_vec())),
+            );
+        }
+
+        Ok(encodings)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_gemma3_embed() {
+        let embedder = Gemma3Embedder::new(
+            "google/embeddinggemma-300m",
+            None,
+            None,
+            Some(crate::Dtype::F32),
+        );
+        let Ok(embedder) = embedder else {
+            return;
+        };
+        let embeddings = embedder
+            .embed(
+                &["Hello, world!", "I am a rust programmer now"],
+                Some(2),
+                None,
+            )
+            .unwrap();
+
+        // Exercise full pipeline and check first 6 dims.
+        // bidirectional transformer -> mean pool
+        // -> dense(768->3072) -> dense(3072->768) -> L2 norm
+        let test_embeddings: Vec<f32> = vec![
+            -0.17819002,
+            0.02147517,
+            0.06739803,
+            -0.03160102,
+            0.02198322,
+            -0.00981485,
+        ];
+        let first_embeddings = embeddings[0].to_dense().unwrap()[0..6].to_vec();
+        println!("{:?}", first_embeddings);
+        assert!(first_embeddings
+		.iter()
+		.zip(test_embeddings.iter())
+		.all(|(a, b)| (a.abs() - b.abs()).abs() < 1e-6));
+        let test_embeddings: Vec<f32> = vec![
+            -0.19414055,
+            -0.01050718,
+            0.02919163,
+            0.0027125,
+            0.037645,
+            0.04710986,
+        ];
+        let second_embeddings = embeddings[1].to_dense().unwrap()[0..6].to_vec();
+        println!("{:?}", second_embeddings);
+        assert!(second_embeddings
+		.iter()
+		.zip(test_embeddings.iter())
+		.all(|(a, b)| (a.abs() - b.abs()).abs() < 1e-6));
+    }
+}
diff --git a/rust/src/embeddings/local/mod.rs b/rust/src/embeddings/local/mod.rs
index 30e174d2..e12e490b 100644
--- a/rust/src/embeddings/local/mod.rs
+++ b/rust/src/embeddings/local/mod.rs
@@ -21,5 +21,6 @@ pub mod ort_bert;
 pub mod ort_jina;
 pub mod pooling;
 pub mod qwen3;
+pub mod gemma3;
 pub mod text_embedding;
 pub mod vision_encoder;
diff --git a/rust/src/models/gemma3.rs b/rust/src/models/gemma3.rs
new file mode 100644
index 00000000..0ca08b2e
--- /dev/null
+++ b/rust/src/models/gemma3.rs
@@ -0,0 +1,515 @@
+//! Gemma LLM architecture (Google) inference implementation.
+//!
+//! See ["Introducing Gemma 3: The most capable model you can run on a single GPU or TPU"](https://blog.google/technology/developers/gemma-3/)
+//!
+//! Based on implementations from HuggingFace transformers.
+
+use std::sync::Arc;
+
+use candle_core::{DType, Device, Module, Result, Tensor, D};
+use candle_nn::{linear_b as linear, Activation, Linear, VarBuilder};
+
+use crate::models::qwen3::repeat_kv;
+
+fn default_rope_local_base_freq() -> f64 {
+    10000.0
+}
+
+#[derive(serde::Deserialize, Debug, Clone)]
+pub struct Config {
+    pub attention_bias: bool,
+    pub head_dim: usize,
+    pub hidden_activation: Activation,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_attention_heads: usize,
+    pub num_hidden_layers: usize,
+    pub num_key_value_heads: usize,
+    pub rms_norm_eps: f64,
+    pub rope_theta: f64,
+    #[serde(default = "default_rope_local_base_freq")]
+    pub rope_local_base_freq: f64,
+    pub vocab_size: usize,
+    pub final_logit_softcapping: Option<f64>,
+    pub attn_logit_softcapping: Option<f64>,
+    pub query_pre_attn_scalar: usize,
+    pub sliding_window: usize,
+    pub _sliding_window_pattern: usize,
+    pub max_position_embeddings: usize,
+}
+
+#[derive(Debug, Clone)]
+struct RmsNorm {
+    weight: Tensor,
+    eps: f64,
+}
+
+impl RmsNorm {
+    fn new(dim: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let weight = vb.get(dim, "weight")?;
+        Ok(Self { weight, eps })
+    }
+}
+
+impl Module for RmsNorm {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x_dtype = x.dtype();
+        let internal_dtype = match x_dtype {
+            DType::F16 | DType::BF16 => DType::F32,
+            d => d,
+        };
+        let hidden_size = x.dim(D::Minus1)?;
+        let x = x.to_dtype(internal_dtype)?;
+        let norm_x = (x.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+        let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
+        x_normed
+            .to_dtype(x_dtype)?
+            .broadcast_mul(&(&self.weight + 1.0)?)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(dtype: DType, rope_theta: f64, cfg: &Config, dev: &Device) -> Result<Self> {
+        let dim = cfg.head_dim;
+        let max_seq_len = cfg.max_position_embeddings;
+        let inv_freq: Vec<_> = (0..dim)
+            .step_by(2)
+            .map(|i| 1f32 / rope_theta.powf(i as f64 / dim as f64) as f32)
+            .collect();
+        let inv_freq_len = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
+        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
+            .to_dtype(dtype)?
+            .reshape((max_seq_len, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        Ok(Self {
+            sin: freqs.sin()?,
+            cos: freqs.cos()?,
+        })
+    }
+
+    fn apply_rotary_emb_qkv(
+        &self,
+        q: &Tensor,
+        k: &Tensor,
+        seqlen_offset: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
+        let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
+        let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
+        let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?;
+        let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?;
+        Ok((q_embed, k_embed))
+    }
+}
+
+#[derive(Debug, Clone)]
+#[allow(clippy::upper_case_acronyms)]
+struct MLP {
+    gate_proj: Linear,
+    up_proj: Linear,
+    down_proj: Linear,
+    act_fn: candle_nn::Activation,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let intermediate_sz = cfg.intermediate_size;
+        let gate_proj = linear(hidden_sz, intermediate_sz, false, vb.pp("gate_proj"))?;
+        let up_proj = linear(hidden_sz, intermediate_sz, false, vb.pp("up_proj"))?;
+        let down_proj = linear(intermediate_sz, hidden_sz, false, vb.pp("down_proj"))?;
+        Ok(Self {
+            gate_proj,
+            up_proj,
+            down_proj,
+            act_fn: cfg.hidden_activation,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?;
+        let rhs = xs.apply(&self.up_proj)?;
+        (lhs * rhs)?.apply(&self.down_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+enum KvCache {
+    Normal(candle_nn::kv_cache::KvCache),
+    Rotating(candle_nn::kv_cache::RotatingKvCache),
+}
+
+#[derive(Debug, Clone)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    q_norm: RmsNorm,
+    k_norm: RmsNorm,
+    num_heads: usize,
+    num_kv_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    attn_logit_softcapping: Option<f64>,
+    rotary_emb: Arc<RotaryEmbedding>,
+    kv_cache: KvCache,
+    use_flash_attn: bool,
+}
+
+impl Attention {
+    fn new(
+        rotary_emb: Arc<RotaryEmbedding>,
+        use_flash_attn: bool,
+        is_sliding: bool,
+        cfg: &Config,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let hidden_sz = cfg.hidden_size;
+        let num_heads = cfg.num_attention_heads;
+        let num_kv_heads = cfg.num_key_value_heads;
+        let num_kv_groups = num_heads / num_kv_heads;
+        let head_dim = cfg.head_dim;
+        let bias = cfg.attention_bias;
+        let q_proj = linear(hidden_sz, num_heads * head_dim, bias, vb.pp("q_proj"))?;
+        let k_proj = linear(hidden_sz, num_kv_heads * head_dim, bias, vb.pp("k_proj"))?;
+        let v_proj = linear(hidden_sz, num_kv_heads * head_dim, bias, vb.pp("v_proj"))?;
+        let o_proj = linear(num_heads * head_dim, hidden_sz, bias, vb.pp("o_proj"))?;
+        let q_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("q_norm"))?;
+        let k_norm = RmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("k_norm"))?;
+        let kv_cache = if is_sliding {
+            KvCache::Rotating(candle_nn::kv_cache::RotatingKvCache::new(
+                2,
+                cfg.sliding_window,
+            ))
+        } else {
+            KvCache::Normal(candle_nn::kv_cache::KvCache::new(2, cfg.sliding_window))
+        };
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            q_norm,
+            k_norm,
+            num_heads,
+            num_kv_heads,
+            num_kv_groups,
+            head_dim,
+            attn_logit_softcapping: cfg.attn_logit_softcapping,
+            rotary_emb,
+            kv_cache,
+            use_flash_attn,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let (b_sz, q_len, _) = xs.dims3()?;
+
+        let query_states = self.q_proj.forward(xs)?;
+        let key_states = self.k_proj.forward(xs)?;
+        let value_states = self.v_proj.forward(xs)?;
+
+        let query_states = query_states
+            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let key_states = key_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let value_states = value_states
+            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let query_states = self.q_norm.forward(&query_states)?;
+        let key_states = self.k_norm.forward(&key_states)?;
+
+        let (query_states, key_states) =
+            self.rotary_emb
+                .apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;
+
+        let (key_states, value_states) = match &mut self.kv_cache {
+            KvCache::Normal(cache) => cache.append(&key_states, &value_states)?,
+            KvCache::Rotating(cache) => cache.append(&key_states, &value_states)?,
+        };
+
+        let key_states = repeat_kv(key_states, self.num_kv_groups)?.contiguous()?;
+        let value_states = repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
+
+        let attn_output = if self.use_flash_attn {
+            // flash-attn expects (b_sz, seq_len, nheads, head_dim)
+            let q = query_states.transpose(1, 2)?;
+            let k = key_states.transpose(1, 2)?;
+            let v = value_states.transpose(1, 2)?;
+            let scale = 1f32 / (self.head_dim as f32).sqrt();
+            flash_attn(&q, &k, &v, scale, attention_mask.is_some())?.transpose(1, 2)?
+        } else {
+            let scale = 1f64 / f64::sqrt(self.head_dim as f64);
+            let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;
+
+            let attn_weights = match self.attn_logit_softcapping {
+                None => attn_weights,
+                Some(sc) => ((attn_weights / sc)?.tanh()? * sc)?,
+            };
+
+            let attn_weights = match attention_mask {
+                None => attn_weights,
+                Some(mask) => attn_weights.broadcast_add(mask)?,
+            };
+            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+            attn_weights.matmul(&value_states)?
+        };
+        attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, q_len, ()))?
+            .apply(&self.o_proj)
+    }
+
+    fn clear_kv_cache(&mut self) {
+        match &mut self.kv_cache {
+            KvCache::Normal(c) => c.reset(),
+            KvCache::Rotating(c) => c.reset(),
+        }
+    }
+}
+
+#[cfg(feature = "flash-attn")]
+fn flash_attn(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    softmax_scale: f32,
+    causal: bool,
+) -> Result<Tensor> {
+    candle_flash_attn::flash_attn(q, k, v, softmax_scale, causal)
+}
+
+#[cfg(not(feature = "flash-attn"))]
+fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Tensor> {
+    unimplemented!("compile with '--features flash-attn'")
+}
+
+#[derive(Debug, Clone)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    input_layernorm: RmsNorm,
+    pre_feedforward_layernorm: RmsNorm,
+    post_feedforward_layernorm: RmsNorm,
+    post_attention_layernorm: RmsNorm,
+    is_sliding: bool,
+}
+
+impl DecoderLayer {
+    fn new(
+        rotary_emb: Arc<RotaryEmbedding>,
+        use_flash_attn: bool,
+        is_sliding: bool,
+        cfg: &Config,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let self_attn = Attention::new(
+            rotary_emb,
+            use_flash_attn,
+            is_sliding,
+            cfg,
+            vb.pp("self_attn"),
+        )?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        let input_layernorm =
+            RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
+        let pre_feedforward_layernorm = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("pre_feedforward_layernorm"),
+        )?;
+        let post_feedforward_layernorm = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_feedforward_layernorm"),
+        )?;
+        let post_attention_layernorm = RmsNorm::new(
+            cfg.hidden_size,
+            cfg.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            pre_feedforward_layernorm,
+            post_feedforward_layernorm,
+            post_attention_layernorm,
+            is_sliding,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self.input_layernorm.forward(xs)?;
+        let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
+        let xs = xs.apply(&self.post_attention_layernorm)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = xs.apply(&self.pre_feedforward_layernorm)?;
+        let xs = xs.apply(&self.mlp)?;
+        let xs = xs.apply(&self.post_feedforward_layernorm)?;
+        residual + xs
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache()
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embed_tokens: candle_nn::Embedding,
+    layers: Vec<DecoderLayer>,
+    norm: RmsNorm,
+    device: Device,
+    dtype: DType,
+    hidden_size: usize,
+    sliding_window: usize,
+}
+
+impl Model {
+    pub fn new(use_flash_attn: bool, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let embed_tokens =
+            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb.pp("embed_tokens"))?;
+        // Gemma3 uses two RoPE base frequencies: a global one (rope_theta) for
+        // full-attention layers and a local one (rope_local_base_freq) for
+        // sliding-attention layers.
+        let global_rotary_emb = Arc::new(RotaryEmbedding::new(
+            vb.dtype(),
+            cfg.rope_theta,
+            cfg,
+            vb.device(),
+        )?);
+        let local_rotary_emb = Arc::new(RotaryEmbedding::new(
+            vb.dtype(),
+            cfg.rope_local_base_freq,
+            cfg,
+            vb.device(),
+        )?);
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_l = vb.pp("layers");
+        for layer_idx in 0..cfg.num_hidden_layers {
+            let is_sliding = (layer_idx + 1) % cfg._sliding_window_pattern > 0;
+            let rotary_emb = if is_sliding {
+                local_rotary_emb.clone()
+            } else {
+                global_rotary_emb.clone()
+            };
+            let layer = DecoderLayer::new(
+                rotary_emb,
+                use_flash_attn,
+                is_sliding,
+                cfg,
+                vb_l.pp(layer_idx),
+            )?;
+            layers.push(layer)
+        }
+        let norm = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("norm"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+            hidden_size: cfg.hidden_size,
+            sliding_window: cfg.sliding_window,
+        })
+    }
+
+    /// Builds an additive attention bias for EmbeddingGemma's bidirectional
+    /// attention (`use_bidirectional_attention=true`): a token may attend to every
+    /// non-padding token. Sliding-attention layers are additionally restricted to a
+    /// symmetric band `|i - j| < window`; full-attention layers pass `window = None`.
+    fn bidirectional_mask(
+        &self,
+        attention_mask: Option<&Tensor>,
+        b_size: usize,
+        tgt_len: usize,
+        window: Option<usize>,
+    ) -> Result<Tensor> {
+        let real: Vec<Vec<bool>> = match attention_mask {
+            Some(am) => am
+                .to_vec2::<u32>()?
+                .into_iter()
+                .map(|row| row.into_iter().map(|v| v != 0).collect())
+                .collect(),
+            None => vec![vec![true; tgt_len]; b_size],
+        };
+        let mut data = Vec::with_capacity(b_size * tgt_len * tgt_len);
+        for row in real.iter().take(b_size) {
+            for i in 0..tgt_len {
+                for (j, &is_real) in row.iter().enumerate() {
+                    let out_of_band = match window {
+                        Some(w) => ((i as i64) - (j as i64)).unsigned_abs() as usize >= w,
+                        None => false,
+                    };
+                    let masked = !is_real || out_of_band;
+                    data.push(if masked { f32::NEG_INFINITY } else { 0.0 });
+                }
+            }
+        }
+        Tensor::from_vec(data, (b_size, 1, tgt_len, tgt_len), &self.device)?.to_dtype(self.dtype)
+    }
+
+    pub fn forward(
+        &mut self,
+        input_ids: &Tensor,
+        attention_mask: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        let (b_size, seq_len) = input_ids.dims2()?;
+        let (full_mask, sliding_mask) = if seq_len <= 1 {
+            (None, None)
+        } else {
+            (
+                Some(self.bidirectional_mask(attention_mask, b_size, seq_len, None)?),
+                Some(self.bidirectional_mask(
+                    attention_mask,
+                    b_size,
+                    seq_len,
+                    Some(self.sliding_window),
+                )?),
+            )
+        };
+        let xs = self.embed_tokens.forward(input_ids)?;
+        let mut xs = (xs * (self.hidden_size as f64).sqrt())?;
+        for layer in self.layers.iter_mut() {
+            let mask = if layer.is_sliding {
+                sliding_mask.as_ref()
+            } else {
+                full_mask.as_ref()
+            };
+            xs = layer.forward(&xs, mask, seqlen_offset)?;
+        }
+        xs.apply(&self.norm)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        for layer in self.layers.iter_mut() {
+            layer.clear_kv_cache()
+        }
+    }
+}
diff --git a/rust/src/models/mod.rs b/rust/src/models/mod.rs
index 695a15c0..78c3fe9f 100644
--- a/rust/src/models/mod.rs
+++ b/rust/src/models/mod.rs
@@ -21,6 +21,7 @@ pub mod clip;
 pub mod colpali;
 pub mod dinov2;
 pub mod gemma;
+pub mod gemma3;
 pub mod idefics3;
 pub mod jina_bert;
 pub mod llama;