diff --git a/t5/.gitignore b/t5/.gitignore
new file mode 100644
index 00000000..ded9aef9
--- /dev/null
+++ b/t5/.gitignore
@@ -0,0 +1 @@
+*.npz
diff --git a/t5/README.md b/t5/README.md
new file mode 100644
index 00000000..a0cc861b
--- /dev/null
+++ b/t5/README.md
@@ -0,0 +1,53 @@
+# T5
+
+The T5 models are encoder-decoder models pre-trained on a mixture of
+unsupervised and supervised tasks.[^1] These models work well on a variety of
+tasks by prepending task-specific prefixes to the input, e.g.:
+`translate English to German: …`, `summarize: ….`, etc.
+
+This example also supports the FLAN-T5 models variants.[^2]
+
+## Setup
+
+Download and convert the model:
+
+```sh
+python convert.py --model <model>
+```
+
+This will make the `<model>.npz` file which MLX can read.
+
+The `<model>` can be any of the following:
+
+| Model Name | Model Size  |
+| ---------- | ----------
+| t5-small   | 60 million  |
+| t5-base    | 220 million |
+| t5-large   | 770 million |
+| t5-3b      | 3 billion   |
+| t5-11b     | 11 billion  |
+
+The FLAN variants can be specified with `google/flan-t5-small`,
+`google/flan-t5-base`, etc. See the [Hugging Face
+page](https://huggingface.co/docs/transformers/model_doc/flan-t5) for a
+complete list of models.
+
+## Generate
+
+Generate text with:
+
+```sh
+python t5.py --model t5-small --prompt "translate English to German: A tasty apple"
+```
+
+This should give the output: `Ein leckerer Apfel`
+
+To see a list of options run:
+
+```sh
+python t5.py --help
+```
+
+[^1]: For more information on T5 see the [original paper](https://arxiv.org/abs/1910.10683)
+   or the [Hugging Face page](https://huggingface.co/docs/transformers/model_doc/t5).
+[^2]: For more information on FLAN-T5 see the [original paper](https://arxiv.org/abs/2210.11416).
diff --git a/t5/convert.py b/t5/convert.py
new file mode 100644
index 00000000..71b009da
--- /dev/null
+++ b/t5/convert.py
@@ -0,0 +1,68 @@
+from transformers import T5ForConditionalGeneration
+import numpy as np
+
+
+SHARED_REPLACEMENT_PATTERNS = [
+    (".block.", ".layers."),
+    (".k.", ".key_proj."),
+    (".o.", ".out_proj."),
+    (".q.", ".query_proj."),
+    (".v.", ".value_proj."),
+    ("shared.", "wte."),
+    ("lm_head.", "lm_head.linear."),
+    (".layer.0.layer_norm.", ".ln1."),
+    (".layer.1.layer_norm.", ".ln2."),
+    (".layer.2.layer_norm.", ".ln3."),
+    (".final_layer_norm.", ".ln."),
+    (
+        "layers.0.layer.0.SelfAttention.relative_attention_bias.",
+        "relative_attention_bias.embeddings.",
+    ),
+]
+
+ENCODER_REPLACEMENT_PATTERNS = [
+    (".layer.0.SelfAttention.", ".attention."),
+    (".layer.1.DenseReluDense.", ".dense."),
+]
+
+DECODER_REPLACEMENT_PATTERNS = [
+    (".layer.0.SelfAttention.", ".self_attention."),
+    (".layer.1.EncDecAttention.", ".cross_attention."),
+    (".layer.2.DenseReluDense.", ".dense."),
+]
+
+
+def replace_key(key: str) -> str:
+    for old, new in SHARED_REPLACEMENT_PATTERNS:
+        key = key.replace(old, new)
+    if key.startswith("encoder."):
+        for old, new in ENCODER_REPLACEMENT_PATTERNS:
+            key = key.replace(old, new)
+    elif key.startswith("decoder."):
+        for old, new in DECODER_REPLACEMENT_PATTERNS:
+            key = key.replace(old, new)
+    return key
+
+
+def convert(model_name):
+    model = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype="auto")
+    weights = {
+        replace_key(k): v.numpy().astype(np.float16)
+        for k, v in model.state_dict().items()
+    }
+    file_name = model_name.replace("/", "-")
+    np.savez(f"{file_name}.npz", **weights)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Convert T5 weights to MLX")
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Name of the T5 model.",
+        default="t5-small",
+    )
+    args = parser.parse_args()
+    convert(args.model)
diff --git a/t5/hf_t5.py b/t5/hf_t5.py
new file mode 100644
index 00000000..ddd99610
--- /dev/null
+++ b/t5/hf_t5.py
@@ -0,0 +1,54 @@
+from transformers import T5ForConditionalGeneration, T5EncoderModel, AutoTokenizer
+
+import argparse
+
+
+def embed(t5_model: str):
+    batch = [
+        "translate English to German: That is good.",
+        "This is an example of T5 working on MLX.",
+    ]
+
+    tokenizer = AutoTokenizer.from_pretrained(t5_model)
+    torch_model = T5EncoderModel.from_pretrained(t5_model)
+    torch_tokens = tokenizer(batch, return_tensors="pt", padding=True)
+    torch_forward = torch_model(**torch_tokens, output_hidden_states=True)
+    torch_output = torch_forward.last_hidden_state.detach().numpy()
+
+    print("\n TF BERT:")
+    for input_str, embedding in list(zip(batch, torch_output)):
+        print("Input:", input_str)
+        print(embedding)
+        print()
+
+
+def generate(t5_model: str):
+    prompt = "translate English to German: As much as six inches of rain could fall in the New York City region through Monday morning, and officials warned of flooding along the coast."
+    tokenizer = AutoTokenizer.from_pretrained(t5_model)
+    torch_model = T5ForConditionalGeneration.from_pretrained(t5_model)
+    torch_tokens = tokenizer(prompt, return_tensors="pt", padding=True).input_ids
+    outputs = torch_model.generate(torch_tokens, do_sample=False, max_length=512)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Run the T5 model using Hugging Face Transformers."
+    )
+    parser.add_argument(
+        "--encode-only",
+        action="store_true",
+        help="Only run the encoder and print the embeddings.",
+        default=False,
+    )
+    parser.add_argument(
+        "--model",
+        default="t5-small",
+        help="The huggingface name of the T5 model to save.",
+    )
+    args = parser.parse_args()
+    if args.encode_only:
+        embed(args.model)
+    else:
+        generate(args.model)
+
diff --git a/t5/requirements.txt b/t5/requirements.txt
new file mode 100644
index 00000000..4a37303a
--- /dev/null
+++ b/t5/requirements.txt
@@ -0,0 +1,3 @@
+mlx
+numpy
+transformers
diff --git a/t5/t5.py b/t5/t5.py
new file mode 100644
index 00000000..6dc5835d
--- /dev/null
+++ b/t5/t5.py
@@ -0,0 +1,469 @@
+import argparse
+from typing import Optional, Tuple, List
+from time import perf_counter_ns
+
+import numpy as np
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_unflatten, tree_map
+from transformers import T5Config, T5Tokenizer
+
+
+def _relative_position_bucket(
+    relative_position, bidirectional=True, num_buckets=32, max_distance=128
+):
+    """
+    Adapted from HF Tensorflow:
+    https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
+
+    Translate relative position to a bucket number for relative attention. The relative position is defined as
+    memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+    position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+    small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+    positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+    This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+    Args:
+        relative_position: an int32 Tensor
+        bidirectional: a boolean - whether the attention is bidirectional
+        num_buckets: an integer
+        max_distance: an integer
+
+    Returns:
+        a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+    """
+    relative_buckets = 0
+    if bidirectional:
+        num_buckets //= 2
+        relative_buckets += (relative_position > 0).astype(mx.int16) * num_buckets
+        relative_position = mx.abs(relative_position)
+    else:
+        relative_position = -mx.minimum(
+            relative_position, mx.zeros_like(relative_position)
+        )
+    # now relative_position is in the range [0, inf)
+
+    # half of the buckets are for exact increments in positions
+    max_exact = num_buckets // 2
+    is_small = relative_position < max_exact
+
+    # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+    scale = (num_buckets - max_exact) / np.log(max_distance / max_exact)
+    relative_position_if_large = max_exact + (
+        mx.log(relative_position.astype(mx.float32) / max_exact) * scale
+    ).astype(mx.int16)
+    relative_position_if_large = mx.minimum(relative_position_if_large, num_buckets - 1)
+    relative_buckets += mx.where(
+        is_small, relative_position, relative_position_if_large
+    )
+    return relative_buckets
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, config: T5Config, bidirectional: bool):
+        self.bidirectional = bidirectional
+        self.num_buckets = config.relative_attention_num_buckets
+        self.max_distance = config.relative_attention_max_distance
+        self.n_heads = config.num_heads
+        self.embeddings = nn.Embedding(
+            config.relative_attention_num_buckets, config.num_heads
+        )
+
+    def __call__(self, query_length: int, key_length: int, offset: int = 0):
+        """Compute binned relative position bias"""
+        context_position = mx.arange(offset, query_length)[:, None]
+        memory_position = mx.arange(key_length)[None, :]
+
+        # shape (query_length, key_length)
+        relative_position = memory_position - context_position
+        relative_position_bucket = _relative_position_bucket(
+            relative_position,
+            bidirectional=self.bidirectional,
+            num_buckets=self.num_buckets,
+            max_distance=self.max_distance,
+        )
+
+        # shape (query_length, key_length, num_heads)
+        values = self.embeddings(relative_position_bucket)
+
+        # shape (num_heads, query_length, key_length)
+        return values.transpose(2, 0, 1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        inner_dim = config.d_kv * config.num_heads
+        self.num_heads = config.num_heads
+        self.query_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.key_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.value_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False)
+
+    def __call__(
+        self,
+        queries: mx.array,
+        keys: mx.array,
+        values: mx.array,
+        mask: Optional[mx.array],
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> [mx.array, Tuple[mx.array, mx.array]]:
+        queries = self.query_proj(queries)
+        keys = self.key_proj(keys)
+        values = self.value_proj(values)
+
+        num_heads = self.num_heads
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 3, 1)
+        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            key_cache, value_cache = cache
+            keys = mx.concatenate([key_cache, keys], axis=3)
+            values = mx.concatenate([value_cache, values], axis=2)
+
+        # Dimensions are [batch x num heads x sequence x hidden dim]
+        queries = queries
+        scores = queries @ keys
+        if mask is not None:
+            scores = scores + mask.astype(scores.dtype)
+
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
+        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.out_proj(values_hat), (keys, values)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dims: int, eps: float = 1e-5):
+        super().__init__()
+        self.weight = mx.ones((dims,))
+        self.eps = eps
+
+    def _norm(self, x):
+        return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
+
+    def __call__(self, x):
+        t = x.dtype
+        output = self._norm(x).astype(t)
+        return self.weight * output
+
+
+class DenseActivation(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        mlp_dims = config.d_ff or config.d_model * 4
+        self.gated = config.feed_forward_proj.startswith("gated")
+        if self.gated:
+            self.wi_0 = nn.Linear(config.d_model, mlp_dims, bias=False)
+            self.wi_1 = nn.Linear(config.d_model, mlp_dims, bias=False)
+        else:
+            self.wi = nn.Linear(config.d_model, mlp_dims, bias=False)
+        self.wo = nn.Linear(mlp_dims, config.d_model, bias=False)
+        activation = config.feed_forward_proj.removeprefix("gated-")
+        if activation == "relu":
+            self.act = nn.relu
+        elif activation == "gelu":
+            self.act = nn.gelu
+        elif activation ==  "silu":
+            self.act = nn.silu
+        else:
+            raise ValueError(f"Unknown activation: {activation}")
+
+    def __call__(self, x):
+        if self.gated:
+            hidden_act = self.act(self.wi_0(x))
+            hidden_linear = self.wi_1(x)
+            x = hidden_act * hidden_linear
+        else:
+            x = self.act(self.wi(x))
+        return self.wo(x)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.attention = MultiHeadAttention(config)
+        self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dense = DenseActivation(config)
+
+    def __call__(self, x, mask):
+        y = self.ln1(x)
+        y, _ = self.attention(y, y, y, mask=mask)
+        x = x + y
+
+        y = self.ln2(x)
+        y = self.dense(y)
+        return x + y
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.layers = [
+            TransformerEncoderLayer(config) for i in range(config.num_layers)
+        ]
+        self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.relative_attention_bias = RelativePositionBias(config, bidirectional=True)
+
+    def __call__(self, x: mx.array):
+        pos_bias = self.relative_attention_bias(x.shape[1], x.shape[1])
+        for layer in self.layers:
+            x = layer(x, mask=pos_bias)
+        return self.ln(x)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.self_attention = MultiHeadAttention(config)
+        self.cross_attention = MultiHeadAttention(config)
+        self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln3 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dense = DenseActivation(config)
+
+    def __call__(
+        self,
+        x: mx.array,
+        memory: mx.array,
+        mask: mx.array,
+        memory_mask: mx.array,
+        cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
+    ):
+        y = self.ln1(x)
+        y, cache = self.self_attention(y, y, y, mask, cache)
+        x = x + y
+
+        y = self.ln2(x)
+        y, _ = self.cross_attention(y, memory, memory, memory_mask)
+        x = x + y
+
+        y = self.ln3(x)
+        y = self.dense(y)
+        x = x + y
+
+        return x, cache
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.layers = [
+            TransformerDecoderLayer(config) for i in range(config.num_layers)
+        ]
+        self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.relative_attention_bias = RelativePositionBias(config, bidirectional=False)
+
+    def __call__(self, x, memory, mask, memory_mask, cache=None):
+        if cache is not None:
+            offset = cache[0][0].shape[3]
+        else:
+            offset = 0
+            cache = [None] * len(self.layers)
+
+        T = offset + x.shape[1]
+        pos_bias = self.relative_attention_bias(T, T, offset=offset)
+        if mask is not None:
+            mask += pos_bias
+        else:
+            mask = pos_bias
+
+        for e, layer in enumerate(self.layers):
+            x, cache[e] = layer(x, memory, mask, memory_mask, cache=cache[e])
+        x = self.ln(x)
+
+        return x, cache
+
+
+class OutputHead(nn.Module):
+    def __init__(self, config: T5Config):
+        self.linear = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+    def __call__(self, inputs):
+        return self.linear(inputs)
+
+
+class T5(nn.Module):
+    def __init__(self, config: T5Config):
+        self.wte = nn.Embedding(config.vocab_size, config.d_model)
+        self.encoder = TransformerEncoder(config)
+        self.decoder = TransformerDecoder(config)
+        self.tie_word_embeddings = config.tie_word_embeddings
+        if not self.tie_word_embeddings:
+            self.lm_head = OutputHead(config)
+        self.model_dim = config.d_model
+
+    def encode(self, inputs: mx.array):
+        return self.encoder(self.wte(inputs))
+
+    def decode(
+        self,
+        inputs: mx.array,
+        memory: mx.array,
+        cache=None,
+    ):
+        inputs = self.wte(inputs)
+        T = inputs.shape[1]
+        if T > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
+            mask = mask.astype(inputs.dtype)
+        else:
+            mask = None
+
+        y, cache = self.decoder(
+            inputs, memory=memory, mask=mask, memory_mask=None, cache=cache
+        )
+        if not self.tie_word_embeddings:
+            y *= self.model_dim**-0.5
+            y = self.lm_head(y)
+        else:
+            y = y @ self.wte.weight.T
+        return y, cache
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        decoder_inputs: mx.array,
+    ):
+        return self.decode(decoder_inputs, self.encode(inputs))[0]
+
+
+class Tokenizer:
+    def __init__(self, model_name: str, config: T5Config):
+        self._decoder_start_id = config.decoder_start_token_id
+        self._tokenizer = T5Tokenizer.from_pretrained(
+            args.model,
+            legacy=False,
+            model_max_length=config.n_positions,
+        )
+
+    @property
+    def eos_id(self) -> int:
+        return self._tokenizer.eos_token_id
+
+    @property
+    def decoder_start_id(self) -> int:
+        return self._decoder_start_id
+
+    def encode(self, s: str) -> mx.array:
+        return mx.array(
+            self._tokenizer(
+                s,
+                return_tensors="np",
+                return_attention_mask=False,
+            )["input_ids"]
+        )
+
+    def decode(self, t: List[int], with_sep: bool = True) -> str:
+        tokens = self._tokenizer.convert_ids_to_tokens(t)
+        return "".join(t.replace("▁", " " if with_sep else "") for t in tokens)
+
+
+def generate(prompt: str, model: T5, tokenizer: Tokenizer, temp: Optional[float] = 0.0):
+    def sample(logits):
+        if temp == 0:
+            return mx.argmax(logits, axis=-1)
+        else:
+            return mx.random.categorical(logits * (1 / temp))
+
+    prompt = tokenizer.encode(prompt)
+    decoder_inputs = mx.array([tokenizer.decoder_start_id])
+    memory = model.encode(prompt)
+    cache = None
+    y = decoder_inputs
+    while True:
+        logits, cache = model.decode(y[None], memory, cache=cache)
+        y = sample(logits[:, -1, :])
+        yield y.squeeze()
+
+
+def load_model(model_name: str, dtype: str = "float16"):
+    config = T5Config.from_pretrained(args.model)
+    dtype = getattr(mx, dtype)
+    model = T5(config)
+    file_name = model_name.replace("/", "-")
+    weights = mx.load(f"{file_name}.npz")
+    weights = tree_unflatten(list(weights.items()))
+    weights = tree_map(lambda p: p.astype(dtype), weights)
+    model.update(weights)
+    mx.eval(model.parameters())
+    return model, Tokenizer(args.model, config)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="T5 Inference script")
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Name of the T5 model.",
+        default="t5-small",
+    )
+    parser.add_argument(
+        "--prompt",
+        help="",
+        default="translate English to German: That is good.",
+    )
+    parser.add_argument(
+        "--encode-only",
+        action="store_true",
+        default=False,
+        help="Whether to decode or not. If true, will output last layer of encoder.",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        "-m",
+        type=int,
+        default=100,
+        help="Maximum number of tokens to generate",
+    )
+    parser.add_argument(
+        "--temp",
+        help="The sampling temperature.",
+        type=float,
+        default=0.0,
+    )
+    parser.add_argument(
+        "--dtype",
+        help="The model data type.",
+        type=str,
+        choices=["float16", "bfloat16", "float32"],
+        default="float32",
+    )
+
+    parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
+    args = parser.parse_args()
+
+    mx.random.seed(args.seed)
+
+    model, tokenizer = load_model(args.model, args.dtype)
+
+    if args.encode_only:
+        print("[INFO] Encoding with T5...", flush=True)
+        print(args.prompt, flush=True)
+        encoder_output = model.encode(tokenizer.encode(args.prompt))
+        print(encoder_output, flush=True)
+        exit(0)
+
+    print("[INFO] Generating with T5...", flush=True)
+    print("Input: ", args.prompt, flush=True)
+
+    start = perf_counter_ns()
+    for token, n_tokens in zip(
+        generate(args.prompt, model, tokenizer, args.temp), range(args.max_tokens)
+    ):
+        if token.item() == tokenizer.eos_id:
+            break
+        print(
+            tokenizer.decode([token.item()], with_sep=n_tokens > 0),
+            end="",
+            flush=True,
+        )
+
+    n_tokens += 1
+    end = perf_counter_ns()
+    elapsed = (end - start) / 1.0e9
+    print()
+    print(f"Time: {elapsed:.2f} seconds, tokens/s: {n_tokens / elapsed:.2f}")