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CLIP

CLIP (Contrastive Language-Image Pre-Training) is a vision-language model trained on 400 million image-text pairs using a contrastive objective. It learns a shared embedding space where matching image-text pairs have high cosine similarity and non-matching pairs have low similarity, enabling zero-shot classification and cross-modal retrieval.

Paper: "Learning Transferable Visual Models From Natural Language Supervision" (Radford et al., 2021) Code: github.com/openai/CLIP

CLIP consists of a Vision Transformer (ViT) image encoder and a causal Transformer text encoder. Both produce fixed-size embeddings that are compared via a temperature-scaled dot product. The jimm implementation supports the full model (CLIP), the vision encoder alone (CLIPVisionModel), and the text encoder alone (CLIPTextModel).

Supported models

HuggingFace ID Vision arch Text hidden_size Image size
openai/clip-vit-base-patch32 ViT-B/32 512 224
openai/clip-vit-base-patch16 ViT-B/16 512 224
openai/clip-vit-large-patch14 ViT-L/14 768 224
openai/clip-vit-large-patch14-336 ViT-L/14 768 336

Note: OpenAI CLIP weights are distributed as pytorch_model.bin. Pass use_pytorch=True when loading.

Basic usage

import jimm
import numpy as np

model = jimm.CLIP.from_pretrained("openai/clip-vit-large-patch14", use_pytorch=True)

images = np.random.rand(4, 224, 224, 3).astype(np.float32)
text = np.array([[49406, 1234, 49407, 0, 0]])  # tokenized text, shape (batch, seq_len)
logits = model(images, text)  # shape: (4, 1)

Flash / Splash Attention

CLIP supports hardware-accelerated attention via Tokamax. Pass an attention_fn at construction time:

Backend Hardware Notes
"mosaic" NVIDIA H100 (SM90) / B100 (SM100) Pallas Mosaic GPU kernel
"triton" Any NVIDIA GPU Pallas Triton kernel
"cudnn" NVIDIA GPU Via JAX-NN / cuDNN
"mosaic_tpu" TPU v5+ (all generations) Splash attention (block-sparse)
"xla_chunked" GPU / TPU Flash-style chunked XLA
"xla" Any Standard XLA fallback 
import jimm

# GPU: try H100 Mosaic kernel, fall back to Triton, then XLA
model = jimm.CLIP.from_pretrained("openai/clip-vit-large-patch14", use_pytorch=True,
                                   attention_fn=jimm.make_tokamax_attention(["mosaic", "triton", "xla"]))

# TPU: try Splash attention, fall back to chunked XLA
model = jimm.CLIP.from_pretrained("openai/clip-vit-large-patch14", use_pytorch=True,
                                   attention_fn=jimm.make_tokamax_attention(["mosaic_tpu", "xla_chunked"]))

You can also apply different kernels to each encoder via vision_attention_fn and text_attention_fn.

Note: Flash/Splash attention does not provide a speedup at typical CLIP context lengths (256 image tokens, 77 text tokens). The primary benefit is memory reduction at longer sequence lengths.

FSDP / Explicit Sharding

CLIP supports JAX explicit sharding (FSDP-style) via CLIPSharding. Large weight matrices are sharded on the contracting (in_features) dimension so that activations carry only the batch-axis sharding.

from jax.experimental import mesh_utils
from jax.sharding import AxisType, Mesh
import jax

n_devices = jax.device_count()
mesh = Mesh(
    mesh_utils.create_device_mesh((1, n_devices)),
    ("data", "fsdp"),
    axis_types=(AxisType.Explicit, AxisType.Explicit),
)
jax.set_mesh(mesh)

model = jimm.CLIP.from_pretrained("openai/clip-vit-large-patch14", use_pytorch=True)
# model params are automatically sharded across fsdp axis

CLIPSharding specs represent per-layer shapes. The Transformer stack prepends None for the scan axis to Variable metadata after nnx.vmap, so the optimizer receives the correct stacked spec natively.

To disable sharding, pass sharding=jimm.common.sharding.NoSharding().

jimm.models.clip.CLIPVisionModel

Bases: Module

Source code in src/jimm/models/clip/clip_model.py 
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class CLIPVisionModel(nnx.Module):
    def __init__(
        self,
        image_resolution: int,
        vision_layers: int,
        vision_hidden_size: int,
        vision_patch_size: int,
        projection_dim: int,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
    ):
        """Initialize the Vision Encoder with projection.

        Args:
            image_resolution (int): The resolution of the input images.
            vision_layers (int): The number of layers in the vision transformer.
            vision_hidden_size (int): The hidden dimension size of the vision transformer.
            vision_patch_size (int): The patch size of the vision transformer.
            projection_dim (int): The output dimension after projection.
            use_gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.
            rngs (rnglib.Rngs | None, optional): The random number generator state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike, optional): The data type for computations. Defaults to jnp.float32.
            param_dtype (DTypeLike, optional): The data type for parameters. Defaults to jnp.float32.
            sharding (ShardingSpec, optional): Sharding specification for parameters. Defaults to CLIPSharding.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        self.vision_layers = vision_layers
        self.vision_hidden_size = vision_hidden_size
        self.vision_patch_size = vision_patch_size
        self.projection_dim = projection_dim
        self.dtype = dtype

        vision_heads = vision_hidden_size // 64

        self.encoder = VisionTransformerBase(
            img_size=image_resolution,
            patch_size=vision_patch_size,
            in_channels=3,
            hidden_size=vision_hidden_size,
            num_layers=vision_layers,
            num_heads=vision_heads,
            mlp_dim=vision_hidden_size * 4,
            use_pre_norm=True,
            use_patch_bias=False,
            act_fn=quickgelu,
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=attention_fn,
            pooling_type="CLS",
            layernorm_epsilon=1e-5,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )
        self.visual_projection = nnx.Linear(
            vision_hidden_size,
            projection_dim,
            use_bias=False,
            dtype=dtype,
            param_dtype=param_dtype,
            rngs=rngs,
            kernel_init=nnx.with_partitioning(
                nnx.initializers.xavier_uniform(),
                sharding.proj_kernel,
            ),
        )

    def __call__(self, image: Float[Array, "batch height width channels"], do_projection: bool = True) -> Float[Array, "batch vision_hidden_size_or_projection_dim"]:
        """Encode images into embeddings.

        Args:
            image (Float[Array, "batch height width channels"]): Batch of input images.
            do_projection (bool): Whether to apply the visual projection layer. Defaults to True.

        Returns:
            Float[Array, "batch vision_hidden_size_or_projection_dim"]: Image embeddings.
            Shape depends on do_projection: vision_hidden_size if False, projection_dim if True.
        """
        features = self.encoder(image)
        if do_projection:
            out_shard = named_sharding_like(features, P(sharding_of(features).spec[0], sharding_of(self.visual_projection.kernel[...]).spec[-1]))
            return self.visual_projection(features, out_sharding=out_shard)
        return features

    @classmethod
    def from_pretrained(
        cls,
        model_name_or_path: str,
        use_pytorch: bool = False,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIPVisionModel":
        """Load a pretrained vision encoder from a CLIP checkpoint.

        Args:
            model_name_or_path (str): Path to local weights or HuggingFace model ID.
            use_pytorch (bool): Whether to load from PyTorch weights. Defaults to False.
            rngs (rnglib.Rngs | None): Random number generator keys. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Data type for computations. Defaults to jnp.float32.
            param_dtype (DTypeLike): Data type for parameters. Defaults to jnp.float32.
            sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
            use_gradient_checkpointing (bool): Whether to use gradient checkpointing. Defaults to False.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

        Returns:
            CLIPVisionModel: Pretrained CLIP vision model
        """
        from .params import load_vision_from_pretrained

        return load_vision_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

    @classmethod
    def from_config(
        cls,
        config: dict[str, Any],
        *,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIPVisionModel":
        """Create model from HuggingFace-compatible config dict.

        Args:
            config (dict[str, Any]): Configuration with "vision_config" and "text_config" keys.
            rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Data type for computations.
            param_dtype (DTypeLike): Data type for parameters.
            sharding (ShardingSpec): Sharding specification for parameters.
            use_gradient_checkpointing (bool): Enable gradient checkpointing.
            attention_fn (Callable[..., Any] | None): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

        Returns:
            CLIPVisionModel: Model with randomly initialized weights.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        vision_config = config["vision_config"]
        text_config = config["text_config"]

        return cls(
            image_resolution=vision_config["image_size"],
            vision_layers=vision_config["num_hidden_layers"],
            vision_hidden_size=vision_config["hidden_size"],
            vision_patch_size=vision_config["patch_size"],
            projection_dim=text_config["hidden_size"],
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )

    def save_pretrained(self, save_directory: str) -> None:
        """Save model weights and config in HuggingFace format.

        Args:
            save_directory (str): Directory path where the model will be saved.
        """
        from .params import save_vision_pretrained

        save_vision_pretrained(self, save_directory)

__call__(image, do_projection=True)

Encode images into embeddings.

Parameters:

Name Type Description Default
image Float[Array, 'batch height width channels']

Batch of input images.

 required
do_projection bool

Whether to apply the visual projection layer. Defaults to True.

 True

Returns:

Type Description
Float[Array, 'batch vision_hidden_size_or_projection_dim']

Float[Array, "batch vision_hidden_size_or_projection_dim"]: Image embeddings.
Float[Array, 'batch vision_hidden_size_or_projection_dim']

Shape depends on do_projection: vision_hidden_size if False, projection_dim if True.
Source code in src/jimm/models/clip/clip_model.py 
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def __call__(self, image: Float[Array, "batch height width channels"], do_projection: bool = True) -> Float[Array, "batch vision_hidden_size_or_projection_dim"]:
    """Encode images into embeddings.

    Args:
        image (Float[Array, "batch height width channels"]): Batch of input images.
        do_projection (bool): Whether to apply the visual projection layer. Defaults to True.

    Returns:
        Float[Array, "batch vision_hidden_size_or_projection_dim"]: Image embeddings.
        Shape depends on do_projection: vision_hidden_size if False, projection_dim if True.
    """
    features = self.encoder(image)
    if do_projection:
        out_shard = named_sharding_like(features, P(sharding_of(features).spec[0], sharding_of(self.visual_projection.kernel[...]).spec[-1]))
        return self.visual_projection(features, out_sharding=out_shard)
    return features

__init__(image_resolution, vision_layers, vision_hidden_size, vision_patch_size, projection_dim, use_gradient_checkpointing=False, attention_fn=None, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding)

Initialize the Vision Encoder with projection.

Parameters:

Name Type Description Default
image_resolution int

The resolution of the input images.

 required
vision_layers int

The number of layers in the vision transformer.

 required
vision_hidden_size int

The hidden dimension size of the vision transformer.

 required
vision_patch_size int

The patch size of the vision transformer.

 required
projection_dim int

The output dimension after projection.

 required
use_gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None
rngs Rngs | None

The random number generator state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

The data type for computations. Defaults to jnp.float32.

 float32
param_dtype DTypeLike

The data type for parameters. Defaults to jnp.float32.

 float32
sharding ShardingSpec

Sharding specification for parameters. Defaults to CLIPSharding.

 CLIPSharding
Source code in src/jimm/models/clip/clip_model.py 
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def __init__(
    self,
    image_resolution: int,
    vision_layers: int,
    vision_hidden_size: int,
    vision_patch_size: int,
    projection_dim: int,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
):
    """Initialize the Vision Encoder with projection.

    Args:
        image_resolution (int): The resolution of the input images.
        vision_layers (int): The number of layers in the vision transformer.
        vision_hidden_size (int): The hidden dimension size of the vision transformer.
        vision_patch_size (int): The patch size of the vision transformer.
        projection_dim (int): The output dimension after projection.
        use_gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.
        rngs (rnglib.Rngs | None, optional): The random number generator state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike, optional): The data type for computations. Defaults to jnp.float32.
        param_dtype (DTypeLike, optional): The data type for parameters. Defaults to jnp.float32.
        sharding (ShardingSpec, optional): Sharding specification for parameters. Defaults to CLIPSharding.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    self.vision_layers = vision_layers
    self.vision_hidden_size = vision_hidden_size
    self.vision_patch_size = vision_patch_size
    self.projection_dim = projection_dim
    self.dtype = dtype

    vision_heads = vision_hidden_size // 64

    self.encoder = VisionTransformerBase(
        img_size=image_resolution,
        patch_size=vision_patch_size,
        in_channels=3,
        hidden_size=vision_hidden_size,
        num_layers=vision_layers,
        num_heads=vision_heads,
        mlp_dim=vision_hidden_size * 4,
        use_pre_norm=True,
        use_patch_bias=False,
        act_fn=quickgelu,
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=attention_fn,
        pooling_type="CLS",
        layernorm_epsilon=1e-5,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )
    self.visual_projection = nnx.Linear(
        vision_hidden_size,
        projection_dim,
        use_bias=False,
        dtype=dtype,
        param_dtype=param_dtype,
        rngs=rngs,
        kernel_init=nnx.with_partitioning(
            nnx.initializers.xavier_uniform(),
            sharding.proj_kernel,
        ),
    )

from_config(config, *, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None) classmethod

Create model from HuggingFace-compatible config dict.

Parameters:

Name Type Description Default
config dict[str, Any]

Configuration with "vision_config" and "text_config" keys.

 required
rngs Rngs | None

Random number generator state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Data type for computations.

 float32
param_dtype DTypeLike

Data type for parameters.

 float32
sharding ShardingSpec

Sharding specification for parameters.

 CLIPSharding
use_gradient_checkpointing bool

Enable gradient checkpointing.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None

Returns:

Name Type Description
CLIPVisionModel CLIPVisionModel

Model with randomly initialized weights.
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_config(
    cls,
    config: dict[str, Any],
    *,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
) -> "CLIPVisionModel":
    """Create model from HuggingFace-compatible config dict.

    Args:
        config (dict[str, Any]): Configuration with "vision_config" and "text_config" keys.
        rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Data type for computations.
        param_dtype (DTypeLike): Data type for parameters.
        sharding (ShardingSpec): Sharding specification for parameters.
        use_gradient_checkpointing (bool): Enable gradient checkpointing.
        attention_fn (Callable[..., Any] | None): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

    Returns:
        CLIPVisionModel: Model with randomly initialized weights.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    vision_config = config["vision_config"]
    text_config = config["text_config"]

    return cls(
        image_resolution=vision_config["image_size"],
        vision_layers=vision_config["num_hidden_layers"],
        vision_hidden_size=vision_config["hidden_size"],
        vision_patch_size=vision_config["patch_size"],
        projection_dim=text_config["hidden_size"],
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )

from_pretrained(model_name_or_path, use_pytorch=False, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None) classmethod

Load a pretrained vision encoder from a CLIP checkpoint.

Parameters:

Name Type Description Default
model_name_or_path str

Path to local weights or HuggingFace model ID.

 required
use_pytorch bool

Whether to load from PyTorch weights. Defaults to False.

 False
rngs Rngs | None

Random number generator keys. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Data type for computations. Defaults to jnp.float32.

 float32
param_dtype DTypeLike

Data type for parameters. Defaults to jnp.float32.

 float32
sharding ShardingSpec

Sharding specification for parameters. Defaults to CLIPSharding.

 CLIPSharding
use_gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None

Returns:

Name Type Description
CLIPVisionModel CLIPVisionModel

Pretrained CLIP vision model
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_pretrained(
    cls,
    model_name_or_path: str,
    use_pytorch: bool = False,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
) -> "CLIPVisionModel":
    """Load a pretrained vision encoder from a CLIP checkpoint.

    Args:
        model_name_or_path (str): Path to local weights or HuggingFace model ID.
        use_pytorch (bool): Whether to load from PyTorch weights. Defaults to False.
        rngs (rnglib.Rngs | None): Random number generator keys. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Data type for computations. Defaults to jnp.float32.
        param_dtype (DTypeLike): Data type for parameters. Defaults to jnp.float32.
        sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
        use_gradient_checkpointing (bool): Whether to use gradient checkpointing. Defaults to False.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

    Returns:
        CLIPVisionModel: Pretrained CLIP vision model
    """
    from .params import load_vision_from_pretrained

    return load_vision_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

save_pretrained(save_directory)

Save model weights and config in HuggingFace format.

Parameters:

Name Type Description Default
save_directory str

Directory path where the model will be saved.

 required
Source code in src/jimm/models/clip/clip_model.py 
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def save_pretrained(self, save_directory: str) -> None:
    """Save model weights and config in HuggingFace format.

    Args:
        save_directory (str): Directory path where the model will be saved.
    """
    from .params import save_vision_pretrained

    save_vision_pretrained(self, save_directory)

jimm.models.clip.CLIPTextModel

Bases: Module

Source code in src/jimm/models/clip/clip_model.py 
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class CLIPTextModel(nnx.Module):
    def __init__(
        self,
        context_length: int,
        vocab_size: int,
        text_hidden_size: int,
        num_text_heads: int,
        num_text_layers: int,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
    ):
        """Initialize CLIP text encoder.

        Args:
            context_length (int): Maximum sequence length.
            vocab_size (int): Size of vocabulary.
            text_hidden_size (int): Hidden dimension size of the text transformer.
            num_text_heads (int): Number of attention heads in the text transformer.
            num_text_layers (int): Number of transformer layers in the text transformer.
            use_gradient_checkpointing (bool): Enable gradient checkpointing.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.
            rngs (rnglib.Rngs | None): RNG state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Computation dtype.
            param_dtype (DTypeLike): Parameter dtype.
            sharding (ShardingSpec): Sharding specification for parameters.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        self.context_length = context_length
        self.vocab_size = vocab_size
        self.text_hidden_size = text_hidden_size
        self.num_text_heads = num_text_heads
        self.num_text_layers = num_text_layers
        self.dtype = dtype

        self.token_embedding = nnx.Embed(
            num_embeddings=vocab_size,
            features=text_hidden_size,
            dtype=dtype,
            param_dtype=param_dtype,
            rngs=rngs,
            embedding_init=nnx.with_partitioning(
                nnx.initializers.xavier_uniform(),
                sharding.embed,
            ),
        )
        self.positional_embedding = nnx.Param(
            nnx.with_partitioning(
                nnx.initializers.truncated_normal(stddev=0.02),
                sharding.text_pos_embed,
            )(rngs.params(), (context_length, text_hidden_size))
        )

        attn_mask = jnp.tril(jnp.ones((context_length, context_length), dtype=dtype))
        self.transformer = Transformer(
            hidden_size=text_hidden_size,
            mlp_dim=text_hidden_size * 4,
            num_layers=num_text_layers,
            num_heads=num_text_heads,
            dropout_rate=0.0,
            attn_mask=attn_mask,
            layernorm_epsilon=1e-5,
            act_fn=quickgelu,
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )

        self.ln_final = nnx.LayerNorm(
            text_hidden_size,
            epsilon=1e-5,
            dtype=dtype,
            param_dtype=param_dtype,
            rngs=rngs,
            scale_init=nnx.with_partitioning(
                nnx.initializers.ones_init(),
                sharding.layernorm,
            ),
            bias_init=nnx.with_partitioning(
                nnx.initializers.zeros_init(),
                sharding.layernorm,
            ),
        )

        self.text_projection = nnx.Linear(
            text_hidden_size,
            text_hidden_size,
            use_bias=False,
            dtype=dtype,
            param_dtype=param_dtype,
            rngs=rngs,
            kernel_init=nnx.with_partitioning(
                nnx.initializers.xavier_uniform(),
                sharding.proj_kernel,
            ),
        )

    def __call__(self, text: Int[Array, "batch context_length"], do_projection: bool = True) -> Float[Array, "batch text_hidden_size"]:
        """Encode text tokens into embeddings.

        Args:
            text (Int[Array, "batch context_length"]): Token sequences.
            do_projection (bool): Apply text projection layer.

        Returns:
            Float[Array, "batch text_hidden_size"]: Text embeddings.
        """
        seq_len = text.shape[1]
        text_sharding = sharding_of(text)
        embed_sharding = named_sharding_like(text, P(*text_sharding.spec, None))
        x = self.token_embedding.embedding[...].at[text].get(out_sharding=embed_sharding)
        pos_embed = jnp.broadcast_to(self.positional_embedding[...][:seq_len], x.shape)
        x = x + reshard_like(pos_embed, x)
        x = self.transformer(x)
        x = self.ln_final(x)

        eot_mask = jax.nn.one_hot(jnp.argmax(text, axis=-1), x.shape[1])
        x_spec = sharding_of(x).spec
        pooled_sharding = named_sharding_like(x, P(x_spec[0], x_spec[2]))
        x = jnp.einsum("bsh,bs->bh", x, eot_mask, out_sharding=pooled_sharding)

        if do_projection:
            out_shard = named_sharding_like(x, P(sharding_of(x).spec[0], sharding_of(self.text_projection.kernel[...]).spec[-1]))
            x = self.text_projection(x, out_sharding=out_shard)
        return x

    @classmethod
    def from_pretrained(
        cls,
        model_name_or_path: str,
        use_pytorch: bool = False,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIPTextModel":
        """Load pretrained text encoder from CLIP checkpoint.

        Args:
            model_name_or_path (str): Local path or HuggingFace model ID.
            use_pytorch (bool): Load from PyTorch weights.
            rngs (rnglib.Rngs | None): RNG state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Computation dtype.
            param_dtype (DTypeLike): Parameter dtype.
            sharding (ShardingSpec): Sharding specification for parameters.
            use_gradient_checkpointing (bool): Enable gradient checkpointing.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

        Returns:
            CLIPTextModel: Pretrained text model.
        """
        from .params import load_text_from_pretrained

        return load_text_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

    @classmethod
    def from_config(
        cls,
        config: dict[str, Any],
        *,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIPTextModel":
        """Create model from HuggingFace-compatible config dict.

        Args:
            config (dict[str, Any]): Configuration with "text_config" key.
            rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Data type for computations.
            param_dtype (DTypeLike): Data type for parameters.
            sharding (ShardingSpec): Sharding specification for parameters.
            use_gradient_checkpointing (bool): Enable gradient checkpointing.
            attention_fn (Callable[..., Any] | None): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

        Returns:
            CLIPTextModel: Model with randomly initialized weights.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        text_config = config["text_config"]

        return cls(
            context_length=text_config["max_position_embeddings"],
            vocab_size=text_config["vocab_size"],
            text_hidden_size=text_config["hidden_size"],
            num_text_heads=text_config["num_attention_heads"],
            num_text_layers=text_config["num_hidden_layers"],
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )

    def save_pretrained(self, save_directory: str) -> None:
        """Save model weights and config in HuggingFace format.

        Args:
            save_directory (str): Directory path where the model will be saved.
        """
        from .params import save_text_pretrained

        save_text_pretrained(self, save_directory)

__call__(text, do_projection=True)

Encode text tokens into embeddings.

Parameters:

Name Type Description Default
text Int[Array, 'batch context_length']

Token sequences.

 required
do_projection bool

Apply text projection layer.

 True

Returns:

Type Description
Float[Array, 'batch text_hidden_size']

Float[Array, "batch text_hidden_size"]: Text embeddings.
Source code in src/jimm/models/clip/clip_model.py 
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def __call__(self, text: Int[Array, "batch context_length"], do_projection: bool = True) -> Float[Array, "batch text_hidden_size"]:
    """Encode text tokens into embeddings.

    Args:
        text (Int[Array, "batch context_length"]): Token sequences.
        do_projection (bool): Apply text projection layer.

    Returns:
        Float[Array, "batch text_hidden_size"]: Text embeddings.
    """
    seq_len = text.shape[1]
    text_sharding = sharding_of(text)
    embed_sharding = named_sharding_like(text, P(*text_sharding.spec, None))
    x = self.token_embedding.embedding[...].at[text].get(out_sharding=embed_sharding)
    pos_embed = jnp.broadcast_to(self.positional_embedding[...][:seq_len], x.shape)
    x = x + reshard_like(pos_embed, x)
    x = self.transformer(x)
    x = self.ln_final(x)

    eot_mask = jax.nn.one_hot(jnp.argmax(text, axis=-1), x.shape[1])
    x_spec = sharding_of(x).spec
    pooled_sharding = named_sharding_like(x, P(x_spec[0], x_spec[2]))
    x = jnp.einsum("bsh,bs->bh", x, eot_mask, out_sharding=pooled_sharding)

    if do_projection:
        out_shard = named_sharding_like(x, P(sharding_of(x).spec[0], sharding_of(self.text_projection.kernel[...]).spec[-1]))
        x = self.text_projection(x, out_sharding=out_shard)
    return x

__init__(context_length, vocab_size, text_hidden_size, num_text_heads, num_text_layers, use_gradient_checkpointing=False, attention_fn=None, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding)

Initialize CLIP text encoder.

Parameters:

Name Type Description Default
context_length int

Maximum sequence length.

 required
vocab_size int

Size of vocabulary.

 required
text_hidden_size int

Hidden dimension size of the text transformer.

 required
num_text_heads int

Number of attention heads in the text transformer.

 required
num_text_layers int

Number of transformer layers in the text transformer.

 required
use_gradient_checkpointing bool

Enable gradient checkpointing.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None
rngs Rngs | None

RNG state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Computation dtype.

 float32
param_dtype DTypeLike

Parameter dtype.

 float32
sharding ShardingSpec

Sharding specification for parameters.

 CLIPSharding
Source code in src/jimm/models/clip/clip_model.py 
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def __init__(
    self,
    context_length: int,
    vocab_size: int,
    text_hidden_size: int,
    num_text_heads: int,
    num_text_layers: int,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
):
    """Initialize CLIP text encoder.

    Args:
        context_length (int): Maximum sequence length.
        vocab_size (int): Size of vocabulary.
        text_hidden_size (int): Hidden dimension size of the text transformer.
        num_text_heads (int): Number of attention heads in the text transformer.
        num_text_layers (int): Number of transformer layers in the text transformer.
        use_gradient_checkpointing (bool): Enable gradient checkpointing.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.
        rngs (rnglib.Rngs | None): RNG state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Computation dtype.
        param_dtype (DTypeLike): Parameter dtype.
        sharding (ShardingSpec): Sharding specification for parameters.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    self.context_length = context_length
    self.vocab_size = vocab_size
    self.text_hidden_size = text_hidden_size
    self.num_text_heads = num_text_heads
    self.num_text_layers = num_text_layers
    self.dtype = dtype

    self.token_embedding = nnx.Embed(
        num_embeddings=vocab_size,
        features=text_hidden_size,
        dtype=dtype,
        param_dtype=param_dtype,
        rngs=rngs,
        embedding_init=nnx.with_partitioning(
            nnx.initializers.xavier_uniform(),
            sharding.embed,
        ),
    )
    self.positional_embedding = nnx.Param(
        nnx.with_partitioning(
            nnx.initializers.truncated_normal(stddev=0.02),
            sharding.text_pos_embed,
        )(rngs.params(), (context_length, text_hidden_size))
    )

    attn_mask = jnp.tril(jnp.ones((context_length, context_length), dtype=dtype))
    self.transformer = Transformer(
        hidden_size=text_hidden_size,
        mlp_dim=text_hidden_size * 4,
        num_layers=num_text_layers,
        num_heads=num_text_heads,
        dropout_rate=0.0,
        attn_mask=attn_mask,
        layernorm_epsilon=1e-5,
        act_fn=quickgelu,
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )

    self.ln_final = nnx.LayerNorm(
        text_hidden_size,
        epsilon=1e-5,
        dtype=dtype,
        param_dtype=param_dtype,
        rngs=rngs,
        scale_init=nnx.with_partitioning(
            nnx.initializers.ones_init(),
            sharding.layernorm,
        ),
        bias_init=nnx.with_partitioning(
            nnx.initializers.zeros_init(),
            sharding.layernorm,
        ),
    )

    self.text_projection = nnx.Linear(
        text_hidden_size,
        text_hidden_size,
        use_bias=False,
        dtype=dtype,
        param_dtype=param_dtype,
        rngs=rngs,
        kernel_init=nnx.with_partitioning(
            nnx.initializers.xavier_uniform(),
            sharding.proj_kernel,
        ),
    )

from_config(config, *, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None) classmethod

Create model from HuggingFace-compatible config dict.

Parameters:

Name Type Description Default
config dict[str, Any]

Configuration with "text_config" key.

 required
rngs Rngs | None

Random number generator state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Data type for computations.

 float32
param_dtype DTypeLike

Data type for parameters.

 float32
sharding ShardingSpec

Sharding specification for parameters.

 CLIPSharding
use_gradient_checkpointing bool

Enable gradient checkpointing.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None

Returns:

Name Type Description
CLIPTextModel CLIPTextModel

Model with randomly initialized weights.
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_config(
    cls,
    config: dict[str, Any],
    *,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
) -> "CLIPTextModel":
    """Create model from HuggingFace-compatible config dict.

    Args:
        config (dict[str, Any]): Configuration with "text_config" key.
        rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Data type for computations.
        param_dtype (DTypeLike): Data type for parameters.
        sharding (ShardingSpec): Sharding specification for parameters.
        use_gradient_checkpointing (bool): Enable gradient checkpointing.
        attention_fn (Callable[..., Any] | None): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

    Returns:
        CLIPTextModel: Model with randomly initialized weights.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    text_config = config["text_config"]

    return cls(
        context_length=text_config["max_position_embeddings"],
        vocab_size=text_config["vocab_size"],
        text_hidden_size=text_config["hidden_size"],
        num_text_heads=text_config["num_attention_heads"],
        num_text_layers=text_config["num_hidden_layers"],
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )

from_pretrained(model_name_or_path, use_pytorch=False, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None) classmethod

Load pretrained text encoder from CLIP checkpoint.

Parameters:

Name Type Description Default
model_name_or_path str

Local path or HuggingFace model ID.

 required
use_pytorch bool

Load from PyTorch weights.

 False
rngs Rngs | None

RNG state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Computation dtype.

 float32
param_dtype DTypeLike

Parameter dtype.

 float32
sharding ShardingSpec

Sharding specification for parameters.

 CLIPSharding
use_gradient_checkpointing bool

Enable gradient checkpointing.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None

Returns:

Name Type Description
CLIPTextModel CLIPTextModel

Pretrained text model.
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_pretrained(
    cls,
    model_name_or_path: str,
    use_pytorch: bool = False,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
) -> "CLIPTextModel":
    """Load pretrained text encoder from CLIP checkpoint.

    Args:
        model_name_or_path (str): Local path or HuggingFace model ID.
        use_pytorch (bool): Load from PyTorch weights.
        rngs (rnglib.Rngs | None): RNG state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Computation dtype.
        param_dtype (DTypeLike): Parameter dtype.
        sharding (ShardingSpec): Sharding specification for parameters.
        use_gradient_checkpointing (bool): Enable gradient checkpointing.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

    Returns:
        CLIPTextModel: Pretrained text model.
    """
    from .params import load_text_from_pretrained

    return load_text_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

save_pretrained(save_directory)

Save model weights and config in HuggingFace format.

Parameters:

Name Type Description Default
save_directory str

Directory path where the model will be saved.

 required
Source code in src/jimm/models/clip/clip_model.py 
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def save_pretrained(self, save_directory: str) -> None:
    """Save model weights and config in HuggingFace format.

    Args:
        save_directory (str): Directory path where the model will be saved.
    """
    from .params import save_text_pretrained

    save_text_pretrained(self, save_directory)

jimm.models.clip.CLIP

Bases: Module

Source code in src/jimm/models/clip/clip_model.py 
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class CLIP(nnx.Module):
    def __init__(
        self,
        image_resolution: int,
        vision_layers: int,
        vision_hidden_size: int,
        vision_patch_size: int,
        context_length: int,
        vocab_size: int,
        text_hidden_size: int,
        num_text_heads: int,
        num_text_layers: int,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
        vision_attention_fn: Callable[..., Any] | None = None,
        text_attention_fn: Callable[..., Any] | None = None,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
    ):
        """Initialize the CLIP model.

        Args:
            image_resolution (int): The resolution of the input images.
            vision_layers (int): The number of layers in the vision transformer.
            vision_hidden_size (int): The hidden dimension size of the vision transformer.
            vision_patch_size (int): The patch size of the vision transformer.
            context_length (int): The maximum sequence length for text.
            vocab_size (int): The size of the vocabulary.
            text_hidden_size (int): The hidden dimension size of the text transformer.
            num_text_heads (int): The number of attention heads in the text transformer.
            num_text_layers (int): The number of layers in the text transformer.
            use_gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function applied to both encoders. Defaults to None.
            vision_attention_fn (Callable[..., Any] | None, optional): Override attention_fn for the vision encoder only. Defaults to None.
            text_attention_fn (Callable[..., Any] | None, optional): Override attention_fn for the text encoder only. Defaults to None.
            rngs (rnglib.Rngs | None, optional): The random number generator state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike, optional): The data type for computations. Defaults to jnp.float32.
            param_dtype (DTypeLike, optional): The data type for parameters. Defaults to jnp.float32.
            sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        self.vision_layers = vision_layers
        self.vision_hidden_size = vision_hidden_size
        self.vision_patch_size = vision_patch_size
        self.context_length = context_length
        self.vocab_size = vocab_size
        self.text_hidden_size = text_hidden_size
        self.num_text_heads = num_text_heads
        self.num_text_layers = num_text_layers
        self.dtype = dtype
        self._original_config = None

        self.vision_model = CLIPVisionModel(
            image_resolution=image_resolution,
            vision_layers=vision_layers,
            vision_hidden_size=vision_hidden_size,
            vision_patch_size=vision_patch_size,
            projection_dim=text_hidden_size,
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=vision_attention_fn or attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )

        self.text_model = CLIPTextModel(
            context_length=context_length,
            vocab_size=vocab_size,
            text_hidden_size=text_hidden_size,
            num_text_heads=num_text_heads,
            num_text_layers=num_text_layers,
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=text_attention_fn or attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )
        self.logit_scale = nnx.Param(nnx.with_partitioning(nnx.initializers.ones_init(), ())(rngs.params(), ()))

    def encode_image(self, image: Float[Array, "batch height width channels"], do_projection: bool = True) -> Float[Array, "batch text_hidden_size"]:
        """Encode images into embeddings.

        Args:
            image (Float[Array, "batch height width channels"]): Batch of input images.
            do_projection (bool): Whether the image encoder should do the visual projection layer. Defaults to true.

        Returns:
            Float[Array, "batch text_hidden_size"]: Image embeddings.
        """
        return self.vision_model(image, do_projection)

    def encode_text(self, text: Int[Array, "batch context_length"]) -> Float[Array, "batch text_hidden_size"]:
        """Encode text tokens into embeddings.

        Args:
            text (Int[Array, "batch context_length"]): Batch of token sequences.

        Returns:
            Float[Array, "batch text_hidden_size"]: Text embeddings.
        """
        return self.text_model(text, do_projection=True)

    def __call__(self, image: Float[Array, "batch height width channels"], text: Int[Array, "batch context_length"]) -> Float[Array, "batch batch"]:
        """Calculate similarity between image and text embeddings.

        Args:
            image (Float[Array, "batch height width channels"]): Batch of input images.
            text (Int[Array, "batch context_length"]): Batch of token sequences.

        Returns:
            Float[Array, "batch batch"]: Similarity scores between all pairs of images and texts.
        """
        image_features: Float[Array, "batch text_hidden_size"] = self.encode_image(image, do_projection=True)
        text_features: Float[Array, "batch text_hidden_size"] = self.encode_text(text)

        image_features: Float[Array, "batch text_hidden_size"] = image_features / jnp.linalg.norm(image_features, axis=-1, keepdims=True)
        text_features: Float[Array, "batch text_hidden_size"] = text_features / jnp.linalg.norm(text_features, axis=-1, keepdims=True)

        logit_scale: Float[Array, ""] = jnp.exp(self.logit_scale[...])
        image_spec = sharding_of(image_features).spec
        logits_sharding = named_sharding_like(image_features, P(image_spec[0], None))
        logits: Float[Array, "batch batch"] = logit_scale * jnp.matmul(image_features, text_features.T, out_sharding=logits_sharding)
        return logits

    @classmethod
    def from_pretrained(
        cls,
        model_name_or_path: str,
        use_pytorch: bool = False,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIP":
        """Load a pretrained CLIP model from a local path or HuggingFace Hub.

        Args:
            model_name_or_path (str): Path to local weights or HuggingFace model ID.
            use_pytorch (bool): Whether to load from PyTorch weights. Defaults to False.
            rngs (rnglib.Rngs | None): Random number generator keys. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Data type for computations. Defaults to jnp.float32.
            param_dtype (DTypeLike): Data type for parameters. Defaults to jnp.float32.
            sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
            use_gradient_checkpointing (bool): Whether to use gradient checkpointing. Defaults to False.
            attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

        Returns:
            CLIP: Pretrained CLIP model
        """
        from .params import load_from_pretrained

        return load_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

    @classmethod
    def from_config(
        cls,
        config: dict[str, Any],
        *,
        rngs: rnglib.Rngs | None = None,
        dtype: DTypeLike = jnp.float32,
        param_dtype: DTypeLike = jnp.float32,
        sharding: ShardingSpec = CLIPSharding,
        use_gradient_checkpointing: bool = False,
        attention_fn: Callable[..., Any] | None = None,
        vision_attention_fn: Callable[..., Any] | None = None,
        text_attention_fn: Callable[..., Any] | None = None,
    ) -> "CLIP":
        """Create model from HuggingFace-compatible config dict.

        Args:
            config (dict[str, Any]): Configuration with "text_config" and "vision_config" keys.
            rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
            dtype (DTypeLike): Data type for computations.
            param_dtype (DTypeLike): Data type for parameters.
            sharding (ShardingSpec): Sharding specification for parameters.
            use_gradient_checkpointing (bool): Enable gradient checkpointing.
            attention_fn (Callable[..., Any] | None): Custom attention function applied to both encoders. Defaults to None.
            vision_attention_fn (Callable[..., Any] | None): Override attention_fn for the vision encoder only. Defaults to None.
            text_attention_fn (Callable[..., Any] | None): Override attention_fn for the text encoder only. Defaults to None.

        Returns:
            CLIP: Model with randomly initialized weights.
        """
        if rngs is None:
            rngs = nnx.Rngs(0)
        text_config = config["text_config"]
        vision_config = config["vision_config"]

        return cls(
            image_resolution=vision_config["image_size"],
            vision_layers=vision_config["num_hidden_layers"],
            vision_hidden_size=vision_config["hidden_size"],
            vision_patch_size=vision_config["patch_size"],
            context_length=text_config["max_position_embeddings"],
            vocab_size=text_config["vocab_size"],
            text_hidden_size=text_config["hidden_size"],
            num_text_heads=text_config["num_attention_heads"],
            num_text_layers=text_config["num_hidden_layers"],
            use_gradient_checkpointing=use_gradient_checkpointing,
            attention_fn=attention_fn,
            vision_attention_fn=vision_attention_fn,
            text_attention_fn=text_attention_fn,
            rngs=rngs,
            dtype=dtype,
            param_dtype=param_dtype,
            sharding=sharding,
        )

    def save_pretrained(self, save_directory: str) -> None:
        """Save the model weights and config in HuggingFace format.

        Args:
            save_directory (str): Directory path where the model will be saved.
        """
        from .params import save_pretrained

        save_pretrained(self, save_directory)

__call__(image, text)

Calculate similarity between image and text embeddings.

Parameters:

Name Type Description Default
image Float[Array, 'batch height width channels']

Batch of input images.

 required
text Int[Array, 'batch context_length']

Batch of token sequences.

 required

Returns:

Type Description
Float[Array, 'batch batch']

Float[Array, "batch batch"]: Similarity scores between all pairs of images and texts.
Source code in src/jimm/models/clip/clip_model.py 
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def __call__(self, image: Float[Array, "batch height width channels"], text: Int[Array, "batch context_length"]) -> Float[Array, "batch batch"]:
    """Calculate similarity between image and text embeddings.

    Args:
        image (Float[Array, "batch height width channels"]): Batch of input images.
        text (Int[Array, "batch context_length"]): Batch of token sequences.

    Returns:
        Float[Array, "batch batch"]: Similarity scores between all pairs of images and texts.
    """
    image_features: Float[Array, "batch text_hidden_size"] = self.encode_image(image, do_projection=True)
    text_features: Float[Array, "batch text_hidden_size"] = self.encode_text(text)

    image_features: Float[Array, "batch text_hidden_size"] = image_features / jnp.linalg.norm(image_features, axis=-1, keepdims=True)
    text_features: Float[Array, "batch text_hidden_size"] = text_features / jnp.linalg.norm(text_features, axis=-1, keepdims=True)

    logit_scale: Float[Array, ""] = jnp.exp(self.logit_scale[...])
    image_spec = sharding_of(image_features).spec
    logits_sharding = named_sharding_like(image_features, P(image_spec[0], None))
    logits: Float[Array, "batch batch"] = logit_scale * jnp.matmul(image_features, text_features.T, out_sharding=logits_sharding)
    return logits

__init__(image_resolution, vision_layers, vision_hidden_size, vision_patch_size, context_length, vocab_size, text_hidden_size, num_text_heads, num_text_layers, use_gradient_checkpointing=False, attention_fn=None, vision_attention_fn=None, text_attention_fn=None, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding)

Initialize the CLIP model.

Parameters:

Name Type Description Default
image_resolution int

The resolution of the input images.

 required
vision_layers int

The number of layers in the vision transformer.

 required
vision_hidden_size int

The hidden dimension size of the vision transformer.

 required
vision_patch_size int

The patch size of the vision transformer.

 required
context_length int

The maximum sequence length for text.

 required
vocab_size int

The size of the vocabulary.

 required
text_hidden_size int

The hidden dimension size of the text transformer.

 required
num_text_heads int

The number of attention heads in the text transformer.

 required
num_text_layers int

The number of layers in the text transformer.

 required
use_gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

 False
attention_fn Callable[..., Any] | None

Custom attention function applied to both encoders. Defaults to None.

 None
vision_attention_fn Callable[..., Any] | None

Override attention_fn for the vision encoder only. Defaults to None.

 None
text_attention_fn Callable[..., Any] | None

Override attention_fn for the text encoder only. Defaults to None.

 None
rngs Rngs | None

The random number generator state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

The data type for computations. Defaults to jnp.float32.

 float32
param_dtype DTypeLike

The data type for parameters. Defaults to jnp.float32.

 float32
sharding ShardingSpec

Sharding specification for parameters. Defaults to CLIPSharding.

 CLIPSharding
Source code in src/jimm/models/clip/clip_model.py 
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def __init__(
    self,
    image_resolution: int,
    vision_layers: int,
    vision_hidden_size: int,
    vision_patch_size: int,
    context_length: int,
    vocab_size: int,
    text_hidden_size: int,
    num_text_heads: int,
    num_text_layers: int,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
    vision_attention_fn: Callable[..., Any] | None = None,
    text_attention_fn: Callable[..., Any] | None = None,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
):
    """Initialize the CLIP model.

    Args:
        image_resolution (int): The resolution of the input images.
        vision_layers (int): The number of layers in the vision transformer.
        vision_hidden_size (int): The hidden dimension size of the vision transformer.
        vision_patch_size (int): The patch size of the vision transformer.
        context_length (int): The maximum sequence length for text.
        vocab_size (int): The size of the vocabulary.
        text_hidden_size (int): The hidden dimension size of the text transformer.
        num_text_heads (int): The number of attention heads in the text transformer.
        num_text_layers (int): The number of layers in the text transformer.
        use_gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function applied to both encoders. Defaults to None.
        vision_attention_fn (Callable[..., Any] | None, optional): Override attention_fn for the vision encoder only. Defaults to None.
        text_attention_fn (Callable[..., Any] | None, optional): Override attention_fn for the text encoder only. Defaults to None.
        rngs (rnglib.Rngs | None, optional): The random number generator state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike, optional): The data type for computations. Defaults to jnp.float32.
        param_dtype (DTypeLike, optional): The data type for parameters. Defaults to jnp.float32.
        sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    self.vision_layers = vision_layers
    self.vision_hidden_size = vision_hidden_size
    self.vision_patch_size = vision_patch_size
    self.context_length = context_length
    self.vocab_size = vocab_size
    self.text_hidden_size = text_hidden_size
    self.num_text_heads = num_text_heads
    self.num_text_layers = num_text_layers
    self.dtype = dtype
    self._original_config = None

    self.vision_model = CLIPVisionModel(
        image_resolution=image_resolution,
        vision_layers=vision_layers,
        vision_hidden_size=vision_hidden_size,
        vision_patch_size=vision_patch_size,
        projection_dim=text_hidden_size,
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=vision_attention_fn or attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )

    self.text_model = CLIPTextModel(
        context_length=context_length,
        vocab_size=vocab_size,
        text_hidden_size=text_hidden_size,
        num_text_heads=num_text_heads,
        num_text_layers=num_text_layers,
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=text_attention_fn or attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )
    self.logit_scale = nnx.Param(nnx.with_partitioning(nnx.initializers.ones_init(), ())(rngs.params(), ()))

encode_image(image, do_projection=True)

Encode images into embeddings.

Parameters:

Name Type Description Default
image Float[Array, 'batch height width channels']

Batch of input images.

 required
do_projection bool

Whether the image encoder should do the visual projection layer. Defaults to true.

 True

Returns:

Type Description
Float[Array, 'batch text_hidden_size']

Float[Array, "batch text_hidden_size"]: Image embeddings.
Source code in src/jimm/models/clip/clip_model.py 
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def encode_image(self, image: Float[Array, "batch height width channels"], do_projection: bool = True) -> Float[Array, "batch text_hidden_size"]:
    """Encode images into embeddings.

    Args:
        image (Float[Array, "batch height width channels"]): Batch of input images.
        do_projection (bool): Whether the image encoder should do the visual projection layer. Defaults to true.

    Returns:
        Float[Array, "batch text_hidden_size"]: Image embeddings.
    """
    return self.vision_model(image, do_projection)

encode_text(text)

Encode text tokens into embeddings.

Parameters:

Name Type Description Default
text Int[Array, 'batch context_length']

Batch of token sequences.

 required

Returns:

Type Description
Float[Array, 'batch text_hidden_size']

Float[Array, "batch text_hidden_size"]: Text embeddings.
Source code in src/jimm/models/clip/clip_model.py 
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def encode_text(self, text: Int[Array, "batch context_length"]) -> Float[Array, "batch text_hidden_size"]:
    """Encode text tokens into embeddings.

    Args:
        text (Int[Array, "batch context_length"]): Batch of token sequences.

    Returns:
        Float[Array, "batch text_hidden_size"]: Text embeddings.
    """
    return self.text_model(text, do_projection=True)

from_config(config, *, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None, vision_attention_fn=None, text_attention_fn=None) classmethod

Create model from HuggingFace-compatible config dict.

Parameters:

Name Type Description Default
config dict[str, Any]

Configuration with "text_config" and "vision_config" keys.

 required
rngs Rngs | None

Random number generator state. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Data type for computations.

 float32
param_dtype DTypeLike

Data type for parameters.

 float32
sharding ShardingSpec

Sharding specification for parameters.

 CLIPSharding
use_gradient_checkpointing bool

Enable gradient checkpointing.

 False
attention_fn Callable[..., Any] | None

Custom attention function applied to both encoders. Defaults to None.

 None
vision_attention_fn Callable[..., Any] | None

Override attention_fn for the vision encoder only. Defaults to None.

 None
text_attention_fn Callable[..., Any] | None

Override attention_fn for the text encoder only. Defaults to None.

 None

Returns:

Name Type Description
CLIP CLIP

Model with randomly initialized weights.
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_config(
    cls,
    config: dict[str, Any],
    *,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
    vision_attention_fn: Callable[..., Any] | None = None,
    text_attention_fn: Callable[..., Any] | None = None,
) -> "CLIP":
    """Create model from HuggingFace-compatible config dict.

    Args:
        config (dict[str, Any]): Configuration with "text_config" and "vision_config" keys.
        rngs (rnglib.Rngs | None): Random number generator state. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Data type for computations.
        param_dtype (DTypeLike): Data type for parameters.
        sharding (ShardingSpec): Sharding specification for parameters.
        use_gradient_checkpointing (bool): Enable gradient checkpointing.
        attention_fn (Callable[..., Any] | None): Custom attention function applied to both encoders. Defaults to None.
        vision_attention_fn (Callable[..., Any] | None): Override attention_fn for the vision encoder only. Defaults to None.
        text_attention_fn (Callable[..., Any] | None): Override attention_fn for the text encoder only. Defaults to None.

    Returns:
        CLIP: Model with randomly initialized weights.
    """
    if rngs is None:
        rngs = nnx.Rngs(0)
    text_config = config["text_config"]
    vision_config = config["vision_config"]

    return cls(
        image_resolution=vision_config["image_size"],
        vision_layers=vision_config["num_hidden_layers"],
        vision_hidden_size=vision_config["hidden_size"],
        vision_patch_size=vision_config["patch_size"],
        context_length=text_config["max_position_embeddings"],
        vocab_size=text_config["vocab_size"],
        text_hidden_size=text_config["hidden_size"],
        num_text_heads=text_config["num_attention_heads"],
        num_text_layers=text_config["num_hidden_layers"],
        use_gradient_checkpointing=use_gradient_checkpointing,
        attention_fn=attention_fn,
        vision_attention_fn=vision_attention_fn,
        text_attention_fn=text_attention_fn,
        rngs=rngs,
        dtype=dtype,
        param_dtype=param_dtype,
        sharding=sharding,
    )

from_pretrained(model_name_or_path, use_pytorch=False, rngs=None, dtype=jnp.float32, param_dtype=jnp.float32, sharding=CLIPSharding, use_gradient_checkpointing=False, attention_fn=None) classmethod

Load a pretrained CLIP model from a local path or HuggingFace Hub.

Parameters:

Name Type Description Default
model_name_or_path str

Path to local weights or HuggingFace model ID.

 required
use_pytorch bool

Whether to load from PyTorch weights. Defaults to False.

 False
rngs Rngs | None

Random number generator keys. If None, initializes to nnx.Rngs(0).

 None
dtype DTypeLike

Data type for computations. Defaults to jnp.float32.

 float32
param_dtype DTypeLike

Data type for parameters. Defaults to jnp.float32.

 float32
sharding ShardingSpec

Sharding specification for parameters. Defaults to CLIPSharding.

 CLIPSharding
use_gradient_checkpointing bool

Whether to use gradient checkpointing. Defaults to False.

 False
attention_fn Callable[..., Any] | None

Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

 None

Returns:

Name Type Description
CLIP CLIP

Pretrained CLIP model
Source code in src/jimm/models/clip/clip_model.py 
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@classmethod
def from_pretrained(
    cls,
    model_name_or_path: str,
    use_pytorch: bool = False,
    rngs: rnglib.Rngs | None = None,
    dtype: DTypeLike = jnp.float32,
    param_dtype: DTypeLike = jnp.float32,
    sharding: ShardingSpec = CLIPSharding,
    use_gradient_checkpointing: bool = False,
    attention_fn: Callable[..., Any] | None = None,
) -> "CLIP":
    """Load a pretrained CLIP model from a local path or HuggingFace Hub.

    Args:
        model_name_or_path (str): Path to local weights or HuggingFace model ID.
        use_pytorch (bool): Whether to load from PyTorch weights. Defaults to False.
        rngs (rnglib.Rngs | None): Random number generator keys. If None, initializes to nnx.Rngs(0).
        dtype (DTypeLike): Data type for computations. Defaults to jnp.float32.
        param_dtype (DTypeLike): Data type for parameters. Defaults to jnp.float32.
        sharding (ShardingSpec): Sharding specification for parameters. Defaults to CLIPSharding.
        use_gradient_checkpointing (bool): Whether to use gradient checkpointing. Defaults to False.
        attention_fn (Callable[..., Any] | None, optional): Custom attention function (e.g. jimm.tokamax_attention or jimm.make_tokamax_attention("mosaic_tpu")). Defaults to None.

    Returns:
        CLIP: Pretrained CLIP model
    """
    from .params import load_from_pretrained

    return load_from_pretrained(cls, model_name_or_path, use_pytorch, rngs, dtype, param_dtype, sharding, use_gradient_checkpointing, attention_fn)

save_pretrained(save_directory)

Save the model weights and config in HuggingFace format.

Parameters:

Name Type Description Default
save_directory str

Directory path where the model will be saved.

 required
Source code in src/jimm/models/clip/clip_model.py 
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def save_pretrained(self, save_directory: str) -> None:
    """Save the model weights and config in HuggingFace format.

    Args:
        save_directory (str): Directory path where the model will be saved.
    """
    from .params import save_pretrained

    save_pretrained(self, save_directory)