GitHub - ritabratamaiti/AnyModal: AnyModal is a Flexible Multimodal Language Model Framework for PyTorch

AnyModal is a modular framework for building multimodal language models in PyTorch. It bridges pretrained vision (or other modality) encoders with large language models via pluggable projector architectures — letting you train a multimodal model with just a config file and a few lines of code.

Key Features

5 Projector Architectures: Linear, MLP, Q-Former (BLIP-2), Perceiver Resampler (Flamingo), C-Abstractor (Honeybee)
Pluggable Encoders: ViT, SigLip, CLIP, DINOv2 — any HuggingFace vision model works out of the box
Config-Driven: YAML configs for all hyperparameters; CLI overrides for experiments
HuggingFace Trainer Integration: LR scheduling, gradient clipping, mixed precision, checkpointing, wandb/tensorboard
Proper Packaging: pip install support, type-annotated API, tested with CI
PEFT/LoRA Support: Optional LoRA on both the vision encoder and language model

Installation

pip install -e .

# With optional dependencies:
pip install -e ".[peft]"          # LoRA support
pip install -e ".[quantization]"  # 4-bit / 8-bit quantization
pip install -e ".[all]"           # Everything

Quick Start

Option 1: Config-Driven (Recommended)

# Train an image captioning model
cd examples/image_captioning
python ../train.py --config config.yaml

# Run inference
python ../inference.py --config config.yaml --model_dir ./output/image_captioning/final

Option 2: Python API

from anymodal import ModelConfig, EncoderConfig, ProjectorConfig, build_model

config = ModelConfig(
    encoder=EncoderConfig(model_name="google/vit-base-patch16-224"),
    projector=ProjectorConfig(type="qformer", kwargs={"num_queries": 32}),
    prompt_text="Describe this image: ",
)

processor, model = build_model(config)
model.print_trainable_parameters()

# Training
logits, loss = model({"input": batch_pixel_values, "text": captions})
loss.backward()

# Inference
generated = model.generate(sample_input, max_new_tokens=100)

Option 3: HuggingFace Trainer

from anymodal import build_model, build_hf_trainer, TrainingConfig, MultiModalDataset

processor, model = build_model(config)

train_dataset = MultiModalDataset(
    dataset_name="AnyModal/flickr30k",
    processor=processor,
    image_field="image",
    text_field="original_alt_text",
    split="train",
)

trainer = build_hf_trainer(
    model, TrainingConfig(num_epochs=3, fp16=True), train_dataset
)
trainer.train()
model.save_pretrained("./output/final")

Projector Architectures

The projector is the key trainable component — it maps encoded modality features into the LLM's embedding space. Choose based on your needs:

Projector	Based On	Resamples?	Best For
`linear`	LLaVA v1	No	Quick experiments, minimal overhead
`mlp`	LLaVA v1.5	No	General-purpose, good default
`qformer`	BLIP-2	Yes → N queries	Fixed output length, cross-attention
`perceiver`	Flamingo	Yes → N latents	Rich latent interactions, longer inputs
`c_abstractor`	Honeybee	Yes → spatial pool	Vision tasks with spatial structure

# Switch projector with one line:
ProjectorConfig(type="perceiver", kwargs={"num_latents": 64, "num_layers": 4})

Architecture

[Input] → Processor → Encoder → Projector → [start_token | projected_tokens | end_token | prompt] → LLM → Text
                                    ↑                                                                  ↑
                            (trainable)                                                    (frozen or LoRA)

Examples

Task	Config	Vision Model	LLM
Image Captioning	config.yaml	ViT-base-224	Llama 3.2-1B
LaTeX OCR	config.yaml	SigLip-384	Llama 3.2-1B + LoRA
LexiCaption	config.yaml	SigLip-384	Llama 3.2-1B + LoRA
Radiology Caption	config.yaml	ViT-base-224 + LoRA	Llama 3.2-1B

Train any example:

cd examples/image_captioning
python ../train.py --config config.yaml --projector_type qformer --num_epochs 5

Model Zoo

Pre-trained models available on HuggingFace:

Image-Captioning-Llama-3.2-1B: ViT + Llama 3.2-1B on Flickr30k

Extending AnyModal

Custom Encoder

from anymodal.encoders import BaseEncoder

class AudioEncoder(BaseEncoder):
    @property
    def hidden_size(self) -> int:
        return 768

    def forward(self, inputs):
        # Your encoding logic
        return features  # (batch, seq_len, 768)

Custom Projector

from anymodal.projectors import BaseProjector

class MyProjector(BaseProjector):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # (batch, seq, input_dim) → (batch, seq, output_dim)
        return self.transform(x)

Development

pip install -e ".[dev]"
pytest tests/ -v
ruff check src/ tests/

Community

License

MIT License. See LICENSE for details.