diff --git a/candle-examples/examples/trocr/assets/trocr.png b/candle-examples/examples/trocr/assets/trocr.png
new file mode 100644
index 00000000..06886aab
Binary files /dev/null and b/candle-examples/examples/trocr/assets/trocr.png differ
diff --git a/candle-examples/examples/trocr/image_processor.rs b/candle-examples/examples/trocr/image_processor.rs
new file mode 100644
index 00000000..531caa56
--- /dev/null
+++ b/candle-examples/examples/trocr/image_processor.rs
@@ -0,0 +1,154 @@
+use image::{DynamicImage, ImageBuffer};
+use serde::Deserialize;
+use std::collections::HashMap;
+
+use candle::{DType, Device, Result, Tensor};
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct ProcessorConfig {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_rescale: bool,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl Default for ProcessorConfig {
+    fn default() -> Self {
+        Self {
+            do_resize: true,
+            height: 384,
+            width: 384,
+            do_rescale: true,
+            do_normalize: true,
+            image_mean: vec![0.5, 0.5, 0.5],
+            image_std: vec![0.5, 0.5, 0.5],
+        }
+    }
+}
+
+pub struct ViTImageProcessor {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl ViTImageProcessor {
+    pub fn new(config: &ProcessorConfig) -> Self {
+        Self {
+            do_resize: config.do_resize,
+            height: config.height,
+            width: config.width,
+            do_normalize: config.do_normalize,
+            image_mean: config.image_mean.clone(),
+            image_std: config.image_std.clone(),
+        }
+    }
+
+    pub fn preprocess(&self, images: Vec<&str>) -> Result<Tensor> {
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let images = self.load_images(images)?;
+
+        let resized_images: Vec<DynamicImage> = if self.do_resize {
+            images
+                .iter()
+                .map(|image| self.resize(image.clone(), None).unwrap())
+                .collect()
+        } else {
+            images
+        };
+
+        let normalized_images: Vec<Tensor> = if self.do_normalize {
+            resized_images
+                .iter()
+                .map(|image| self.normalize(image.clone(), None, None).unwrap())
+                .collect()
+        } else {
+            let resized_images: Vec<ImageBuffer<image::Rgb<u8>, Vec<u8>>> =
+                resized_images.iter().map(|image| image.to_rgb8()).collect();
+            let data = resized_images
+                .into_iter()
+                .map(|image| image.into_raw())
+                .collect::<Vec<Vec<u8>>>();
+
+            data.iter()
+                .map(|image| {
+                    Tensor::from_vec(image.clone(), (height, width, channels), &Device::Cpu)
+                        .unwrap()
+                        .permute((2, 0, 1))
+                        .unwrap()
+                })
+                .collect::<Vec<Tensor>>()
+        };
+
+        Tensor::stack(&normalized_images, 0)
+    }
+
+    fn resize(
+        &self,
+        image: image::DynamicImage,
+        size: Option<HashMap<String, u32>>,
+    ) -> Result<image::DynamicImage> {
+        let (height, width) = match &size {
+            Some(size) => (size.get("height").unwrap(), size.get("width").unwrap()),
+            None => (&self.height, &self.width),
+        };
+
+        let resized_image =
+            image.resize_exact(*width, *height, image::imageops::FilterType::Triangle);
+
+        Ok(resized_image)
+    }
+
+    fn normalize(
+        &self,
+        image: image::DynamicImage,
+        mean: Option<Vec<f32>>,
+        std: Option<Vec<f32>>,
+    ) -> Result<Tensor> {
+        let mean = match mean {
+            Some(mean) => mean,
+            None => self.image_mean.clone(),
+        };
+
+        let std = match std {
+            Some(std) => std,
+            None => self.image_std.clone(),
+        };
+
+        let mean = Tensor::from_vec(mean, (3, 1, 1), &Device::Cpu)?;
+        let std = Tensor::from_vec(std, (3, 1, 1), &Device::Cpu)?;
+
+        let image = image.to_rgb8();
+        let data = image.into_raw();
+
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let data =
+            Tensor::from_vec(data, &[height, width, channels], &Device::Cpu)?.permute((2, 0, 1))?;
+
+        (data.to_dtype(DType::F32)? / 255.)?
+            .broadcast_sub(&mean)?
+            .broadcast_div(&std)
+    }
+
+    pub fn load_images(&self, image_path: Vec<&str>) -> Result<Vec<image::DynamicImage>> {
+        let mut images: Vec<image::DynamicImage> = Vec::new();
+        for path in image_path {
+            let img = image::io::Reader::open(path)?.decode().unwrap();
+            images.push(img);
+        }
+
+        Ok(images)
+    }
+}
diff --git a/candle-examples/examples/trocr/main.rs b/candle-examples/examples/trocr/main.rs
new file mode 100644
index 00000000..e93d6b2f
--- /dev/null
+++ b/candle-examples/examples/trocr/main.rs
@@ -0,0 +1,132 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::trocr;
+
+use tokenizers::Tokenizer;
+mod image_processor;
+
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    Base,
+    Large,
+}
+
+#[derive(Parser, Debug)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    /// Choose the variant of the model to run.
+    #[arg(long, default_value = "base")]
+    which: Which,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Text to be translated
+    #[arg(long)]
+    image: String,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
+    let args = Args::parse();
+
+    let tokenizer_dec = {
+        let tokenizer = Api::new()?
+            .model(String::from("ToluClassics/candle-trocr-tokenizer"))
+            .get("tokenizer.json")?;
+
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let vb = {
+        let model = match args.model {
+            Some(model) => std::path::PathBuf::from(model),
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-base-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/3".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Large => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-large-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/6".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+            },
+        };
+        println!("model: {:?}", model);
+        unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
+    };
+
+    let encoder_config = match args.which {
+        Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
+        Which::Large => {
+            candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
+        }
+    };
+
+    let decoder_config = trocr::TrOCRConfig::default();
+    let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
+
+    let config = image_processor::ProcessorConfig::default();
+    let processor = image_processor::ViTImageProcessor::new(&config);
+
+    let image = vec![args.image.as_str()];
+    let image = processor.preprocess(image)?;
+
+    let encoder_xs = model.encoder().forward(&image)?;
+
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let mut token_ids: Vec<u32> = vec![decoder_config.decoder_start_token_id];
+    for index in 0..1000 {
+        let context_size = if index >= 1 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+
+        let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
+
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        token_ids.push(token);
+
+        if let Some(t) = tokenizer_dec.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        if token == decoder_config.eos_token_id {
+            break;
+        }
+    }
+
+    if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+
+    Ok(())
+}
diff --git a/candle-examples/examples/trocr/readme.md b/candle-examples/examples/trocr/readme.md
new file mode 100644
index 00000000..329940f8
--- /dev/null
+++ b/candle-examples/examples/trocr/readme.md
@@ -0,0 +1,16 @@
+# candle-trocr
+
+`TrOCR` is a transformer OCR Model. In this example it is used to
+transcribe image text. See the associated [model
+card](https://huggingface.co/microsoft/trocr-base-printed) for details on
+the model itself.
+
+## Running an example
+
+```bash
+cargo run --example trocr --release --  --which base --cpu --image assets/trocr.png
+```
+
+```
+<s> industry , Mr. Brown commented icily . " Let us have a</s>
+```
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 370b9108..3c025660 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -29,6 +29,7 @@ pub mod segment_anything;
 pub mod stable_diffusion;
 pub mod stable_lm;
 pub mod t5;
+pub mod trocr;
 pub mod vgg;
 pub mod vit;
 pub mod whisper;
diff --git a/candle-transformers/src/models/trocr.rs b/candle-transformers/src/models/trocr.rs
new file mode 100644
index 00000000..785b06ca
--- /dev/null
+++ b/candle-transformers/src/models/trocr.rs
@@ -0,0 +1,434 @@
+use crate::models::vit::{Config, Embeddings, Encoder};
+use candle::{Result, Tensor};
+use candle_nn::{
+    embedding, layer_norm, linear_no_bias, Embedding, LayerNorm, Linear, Module, VarBuilder,
+};
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct TrOCRConfig {
+    pub vocab_size: usize,
+    pub d_model: usize,
+    pub hidden_size: usize,
+    pub decoder_layers: usize,
+    pub decoder_attention_heads: usize,
+    pub decoder_ffn_dim: usize,
+    pub activation_function: candle_nn::Activation,
+    pub max_position_embeddings: usize,
+    pub dropout: f64,
+    pub attention_dropout: f64,
+    pub activation_dropout: f64,
+    pub decoder_start_token_id: u32,
+    pub init_std: f64,
+    pub decoder_layerdrop: f64,
+    pub use_cache: bool,
+    pub scale_embedding: bool,
+    pub use_learned_position_embeddings: bool,
+    pub layernorm_embedding: bool,
+    pub pad_token_id: usize,
+    pub bos_token_id: usize,
+    pub eos_token_id: u32,
+    pub num_attention_heads: usize,
+    pub decoder_vocab_size: Option<usize>,
+}
+
+impl Default for TrOCRConfig {
+    fn default() -> Self {
+        Self {
+            vocab_size: 50265,
+            d_model: 1024,
+            hidden_size: 768,
+            decoder_layers: 12,
+            decoder_attention_heads: 16,
+            decoder_ffn_dim: 4096,
+            activation_function: candle_nn::Activation::Gelu,
+            max_position_embeddings: 512,
+            dropout: 0.1,
+            attention_dropout: 0.0,
+            activation_dropout: 0.0,
+            decoder_start_token_id: 2,
+            init_std: 0.02,
+            decoder_layerdrop: 0.0,
+            use_cache: true,
+            scale_embedding: false,
+            use_learned_position_embeddings: true,
+            layernorm_embedding: true,
+            pad_token_id: 1,
+            bos_token_id: 0,
+            eos_token_id: 2,
+            num_attention_heads: 12,
+            decoder_vocab_size: Some(50265),
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRLearnedPositionalEmbedding {
+    offset: usize,
+    weights: Embedding,
+}
+
+impl TrOCRLearnedPositionalEmbedding {
+    fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
+        let offset: usize = 2;
+        let num_embeddings = cfg.max_position_embeddings;
+        let embedding_dim = cfg.d_model;
+        let weights = embedding(num_embeddings + offset, embedding_dim, vb)?;
+
+        Ok(Self { offset, weights })
+    }
+
+    fn forward(&mut self, input_ids: &Tensor, past_key_values_length: u32) -> Result<Tensor> {
+        let (b_sz, seq_len) = input_ids.dims2()?;
+
+        let mut positions = Tensor::arange(
+            past_key_values_length,
+            seq_len as u32 + past_key_values_length,
+            input_ids.device(),
+        )?
+        .expand((b_sz, seq_len))?;
+
+        positions =
+            positions.broadcast_add(&Tensor::new(self.offset as u32, input_ids.device())?)?;
+        self.weights.forward(&positions)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRAttention {
+    head_dim: usize,
+    num_heads: usize,
+    is_decoder: bool,
+    scaling: f64,
+    k_proj: Linear,
+    v_proj: Linear,
+    q_proj: Linear,
+    out_proj: Linear,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl TrOCRAttention {
+    fn load(
+        vb: VarBuilder,
+        cfg: &TrOCRConfig,
+        kdim: Option<usize>,
+        vdim: Option<usize>,
+    ) -> Result<Self> {
+        let embed_dim = cfg.d_model;
+        let num_heads = cfg.decoder_attention_heads;
+        let head_dim = embed_dim / num_heads;
+        let kdim = kdim.unwrap_or(embed_dim);
+        let vdim = vdim.unwrap_or(embed_dim);
+
+        let k_proj = linear_no_bias(kdim, embed_dim, vb.pp("k_proj"))?;
+        let v_proj = linear_no_bias(vdim, embed_dim, vb.pp("v_proj"))?;
+        let q_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("q_proj"))?;
+
+        let out_proj = linear_no_bias(embed_dim, embed_dim, vb.pp("out_proj"))?;
+        Ok(Self {
+            head_dim,
+            num_heads,
+            is_decoder: true,
+            scaling: 1. / (head_dim as f64).sqrt(),
+            k_proj,
+            v_proj,
+            q_proj,
+            out_proj,
+            kv_cache: None,
+        })
+    }
+
+    fn _shape(&self, tensor: &Tensor, bsz: usize) -> Result<Tensor> {
+        tensor
+            .reshape((bsz, (), self.num_heads, self.head_dim))?
+            .transpose(1, 2)?
+            .contiguous()
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        kv_states: Option<&Tensor>,
+        attn_mask: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let (b_sz, tgt_len, _) = xs.dims3()?;
+        let query_states = (xs.apply(&self.q_proj)? * self.scaling)?;
+        let (key_states, value_states) = match kv_states {
+            None => {
+                let key_states = self._shape(&xs.apply(&self.k_proj)?, b_sz)?;
+                let value_states = self._shape(&xs.apply(&self.v_proj)?, b_sz)?;
+                if self.is_decoder {
+                    let kv_states = match &self.kv_cache {
+                        None => (key_states, value_states),
+                        Some((p_key_states, p_value_states)) => {
+                            let key_states = Tensor::cat(&[p_key_states, &key_states], 2)?;
+                            let value_states = Tensor::cat(&[p_value_states, &value_states], 2)?;
+                            (key_states, value_states)
+                        }
+                    };
+                    self.kv_cache = Some(kv_states.clone());
+                    kv_states
+                } else {
+                    (key_states, value_states)
+                }
+            }
+            Some(kv_states) => {
+                let key_states = self._shape(&kv_states.apply(&self.k_proj)?, b_sz)?;
+                let value_states = self._shape(&kv_states.apply(&self.v_proj)?, b_sz)?;
+                (key_states, value_states)
+            }
+        };
+        let proj_shape = (b_sz * self.num_heads, (), self.head_dim);
+        let query_states = self._shape(&query_states, b_sz)?.reshape(proj_shape)?;
+        let key_states = key_states.reshape(proj_shape)?;
+        let value_states = value_states.reshape(proj_shape)?;
+        let attn_weights = query_states.matmul(&key_states.transpose(1, 2)?)?;
+        let attn_weights = match attn_mask {
+            None => attn_weights,
+            Some(attn_mask) => attn_weights.broadcast_add(attn_mask)?,
+        };
+        let attn_probs = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+        let attn_output = attn_probs.matmul(&value_states)?;
+        attn_output
+            .reshape((b_sz, self.num_heads, tgt_len, self.head_dim))?
+            .transpose(1, 2)?
+            .reshape((b_sz, tgt_len, self.head_dim * self.num_heads))?
+            .apply(&self.out_proj)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct TrOCRDecoderLayer {
+    self_attn: TrOCRAttention,
+    activation_fn: candle_nn::Activation,
+    self_attn_layer_norm: LayerNorm,
+    encoder_attn: TrOCRAttention,
+    encoder_attn_layer_norm: LayerNorm,
+    fc1: Linear,
+    fc2: Linear,
+    final_layer_norm: LayerNorm,
+}
+
+impl TrOCRDecoderLayer {
+    fn load(vb: VarBuilder, cfg: &TrOCRConfig) -> Result<Self> {
+        let embed_dim = cfg.d_model;
+        let self_attn = TrOCRAttention::load(vb.pp("self_attn"), cfg, None, None)?;
+        let self_attn_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("self_attn_layer_norm"))?;
+        let encoder_attn = TrOCRAttention::load(
+            vb.pp("encoder_attn"),
+            cfg,
+            Some(cfg.hidden_size),
+            Some(cfg.hidden_size),
+        )?;
+        let encoder_attn_layer_norm =
+            layer_norm(embed_dim, 1e-5, vb.pp("encoder_attn_layer_norm"))?;
+        let fc1 = linear_no_bias(embed_dim, cfg.decoder_ffn_dim, vb.pp("fc1"))?;
+        let fc2 = linear_no_bias(cfg.decoder_ffn_dim, embed_dim, vb.pp("fc2"))?;
+        let final_layer_norm = layer_norm(embed_dim, 1e-5, vb.pp("final_layer_norm"))?;
+        let activation_fn = candle_nn::Activation::Gelu;
+
+        Ok(Self {
+            self_attn,
+            activation_fn,
+            self_attn_layer_norm,
+            encoder_attn,
+            encoder_attn_layer_norm,
+            fc1,
+            fc2,
+            final_layer_norm,
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: &Tensor,
+        encoder_hidden_states: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let residual = xs.clone();
+        let xs = self.self_attn.forward(xs, None, Some(attention_mask))?;
+        let xs = (xs + residual)?;
+        let mut xs = self.self_attn_layer_norm.forward(&xs)?;
+
+        if let Some(encoder_hidden_states) = &encoder_hidden_states {
+            let residual = xs.clone();
+            let encoder_attention_mask = attention_mask.clone(); // TODO
+            xs = self.encoder_attn.forward(
+                &xs,
+                Some(encoder_hidden_states),
+                Some(&encoder_attention_mask),
+            )?;
+            xs = (xs + residual)?;
+            xs = self.encoder_attn_layer_norm.forward(&xs)?
+        }
+
+        let residual = xs.clone();
+        let xs = self.fc1.forward(&xs)?;
+        let xs = self.activation_fn.forward(&xs)?;
+        let xs = self.fc2.forward(&xs)?;
+        let xs = (xs + residual)?;
+        let xs = self.final_layer_norm.forward(&xs)?;
+
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRDecoder {
+    layers: Vec<TrOCRDecoderLayer>,
+    embed_scale: Option<f64>,
+    embed_tokens: Embedding,
+    embed_positions: TrOCRLearnedPositionalEmbedding,
+}
+
+impl TrOCRDecoder {
+    fn new(cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let vb = vb.pp("decoder.model.decoder");
+
+        let embed_tokens = embedding(cfg.vocab_size, cfg.d_model, vb.pp("embed_tokens"))?;
+        let embed_positions = TrOCRLearnedPositionalEmbedding::load(vb.pp("embed_positions"), cfg)?;
+        let mut layers = Vec::with_capacity(cfg.decoder_layers);
+        let vb_l = vb.pp("layers");
+        for idx in 0..cfg.decoder_layers {
+            let layer = TrOCRDecoderLayer::load(vb_l.pp(idx), cfg)?;
+            layers.push(layer)
+        }
+        let embed_scale = if cfg.scale_embedding {
+            Some((cfg.d_model as f64).sqrt())
+        } else {
+            None
+        };
+
+        Ok(Self {
+            layers,
+            embed_scale,
+            embed_tokens,
+            embed_positions,
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: Option<&Tensor>,
+        past_kv_len: usize,
+        attn_mask: &Tensor,
+    ) -> Result<Tensor> {
+        let embed_pos = self.embed_positions.forward(xs, past_kv_len as u32)?;
+        let xs = xs.apply(&self.embed_tokens)?;
+
+        let xs = match self.embed_scale {
+            None => xs,
+            Some(scale) => (xs * scale)?,
+        };
+
+        let mut xs = xs.broadcast_add(&embed_pos)?;
+
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, attn_mask, encoder_xs)?;
+        }
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCREncoder {
+    embeddings: Embeddings,
+    encoder: Encoder,
+    layernorm: LayerNorm,
+}
+
+impl TrOCREncoder {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_v = vb.pp("encoder");
+
+        let embeddings = Embeddings::new(cfg, false, vb_v.pp("embeddings"))?;
+
+        let encoder = Encoder::new(cfg, vb_v.pp("encoder"))?;
+        let layernorm = layer_norm(cfg.hidden_size, cfg.layer_norm_eps, vb_v.pp("layernorm"))?;
+
+        Ok(Self {
+            embeddings,
+            encoder,
+            layernorm,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let embedding_output = self.embeddings.forward(xs, None, false)?;
+        let encoder_outputs = self.encoder.forward(&embedding_output)?;
+
+        self.layernorm.forward(&encoder_outputs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRForCausalLM {
+    decoder: TrOCRDecoder,
+    output_projection: Linear,
+}
+
+impl TrOCRForCausalLM {
+    pub fn new(decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let decoder = TrOCRDecoder::new(decoder_cfg, vb.clone())?;
+        let output_projection =
+            candle_nn::Linear::new(decoder.embed_tokens.embeddings().clone(), None);
+        Ok(Self {
+            decoder,
+            output_projection,
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: Option<&Tensor>,
+        past_kv_len: usize,
+        attn_mask: &Tensor,
+    ) -> Result<Tensor> {
+        let xs = self
+            .decoder
+            .forward(xs, encoder_xs, past_kv_len, attn_mask)?;
+        let xs = xs.apply(&self.output_projection)?;
+
+        Ok(xs)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct TrOCRModel {
+    encoder: TrOCREncoder,
+    decoder: TrOCRForCausalLM,
+}
+
+impl TrOCRModel {
+    pub fn new(encoder_cfg: &Config, decoder_cfg: &TrOCRConfig, vb: VarBuilder) -> Result<Self> {
+        let encoder = TrOCREncoder::new(encoder_cfg, vb.clone())?;
+        let decoder = TrOCRForCausalLM::new(decoder_cfg, vb)?;
+        Ok(Self { encoder, decoder })
+    }
+
+    pub fn encoder(&mut self) -> &mut TrOCREncoder {
+        &mut self.encoder
+    }
+
+    pub fn decoder(&mut self) -> &mut TrOCRForCausalLM {
+        &mut self.decoder
+    }
+
+    pub fn decode(
+        &mut self,
+        xs: &Tensor,
+        encoder_xs: &Tensor,
+        past_kv_len: usize,
+    ) -> Result<Tensor> {
+        let seq_len = xs.dim(1)?;
+        let mask: Vec<_> = (0..seq_len)
+            .flat_map(|i| (0..seq_len).map(move |j| if j > i { f32::NEG_INFINITY } else { 0f32 }))
+            .collect();
+        let mask = Tensor::from_vec(mask, (seq_len, seq_len), xs.device())?;
+
+        self.decoder
+            .forward(xs, Some(encoder_xs), past_kv_len, &mask)
+    }
+}
diff --git a/candle-transformers/src/models/vit.rs b/candle-transformers/src/models/vit.rs
index e2218c54..962528c1 100644
--- a/candle-transformers/src/models/vit.rs
+++ b/candle-transformers/src/models/vit.rs
@@ -6,16 +6,16 @@ use candle_nn::{layer_norm, LayerNorm, VarBuilder};
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/configuration_vit.py
 #[derive(Debug, Clone)]
 pub struct Config {
-    hidden_size: usize,
-    num_hidden_layers: usize,
-    num_attention_heads: usize,
-    intermediate_size: usize,
-    hidden_act: candle_nn::Activation,
-    layer_norm_eps: f64,
-    image_size: usize,
-    patch_size: usize,
-    num_channels: usize,
-    qkv_bias: bool,
+    pub hidden_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub intermediate_size: usize,
+    pub hidden_act: candle_nn::Activation,
+    pub layer_norm_eps: f64,
+    pub image_size: usize,
+    pub patch_size: usize,
+    pub num_channels: usize,
+    pub qkv_bias: bool,
 }
 
 impl Config {
@@ -34,6 +34,21 @@ impl Config {
             qkv_bias: true,
         }
     }
+
+    pub fn microsoft_trocr_base_handwritten() -> Self {
+        Self {
+            hidden_size: 768,
+            num_hidden_layers: 12,
+            num_attention_heads: 12,
+            intermediate_size: 3072,
+            hidden_act: candle_nn::Activation::Gelu,
+            layer_norm_eps: 1e-12,
+            image_size: 384,
+            patch_size: 16,
+            num_channels: 3,
+            qkv_bias: false,
+        }
+    }
 }
 
 #[derive(Debug, Clone)]
@@ -76,7 +91,7 @@ impl Module for PatchEmbeddings {
 }
 
 #[derive(Debug, Clone)]
-struct Embeddings {
+pub struct Embeddings {
     cls_token: Tensor,
     mask_token: Option<Tensor>,
     patch_embeddings: PatchEmbeddings,
@@ -85,7 +100,7 @@ struct Embeddings {
 }
 
 impl Embeddings {
-    fn new(cfg: &Config, use_mask_token: bool, vb: VarBuilder) -> Result<Self> {
+    pub fn new(cfg: &Config, use_mask_token: bool, vb: VarBuilder) -> Result<Self> {
         let hidden_size = cfg.hidden_size;
         let cls_token = vb.get((1, 1, hidden_size), "cls_token")?;
         let mask_token = if use_mask_token {
@@ -115,7 +130,7 @@ impl Embeddings {
         todo!()
     }
 
-    fn forward(
+    pub fn forward(
         &self,
         pixel_values: &Tensor,
         bool_masked_pos: Option<&Tensor>,
@@ -324,12 +339,12 @@ impl Module for Layer {
 }
 
 #[derive(Debug, Clone)]
-struct Encoder {
+pub struct Encoder {
     layers: Vec<Layer>,
 }
 
 impl Encoder {
-    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
         let vb = vb.pp("layer");
         let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
         for i in 0..cfg.num_hidden_layers {