FastVLM: Efficient Vision Encoding for Vision Language Models

FastVLM was introduced in FastVLM: Efficient Vision Encoding for Vision Language Models. (CVPR 2025)

Highlights

• We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images.
• Our smallest variant outperforms LLaVA-OneVision-0.5B with 85x faster Time-to-First-Token (TTFT) and 3.4x smaller vision encoder.
• Our larger variants using Qwen2-7B LLM outperform recent works like Cambrian-1-8B while using a single image encoder with a 7.9x faster TTFT.

Evaluations

Usage Example

To run inference of PyTorch checkpoint, follow the instruction in the official repo:

Download the model

huggingface-cli download apple/FastVLM-7B

Run inference using predict.py from the official repo.

python predict.py --model-path /path/to/checkpoint-dir \
                  --image-file /path/to/image.png \
                  --prompt "Describe the image."

Run inference with Transformers (Remote Code)

To run inference with transformers we can leverage trust_remote_code along with the following snippet:

import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
MID = "apple/FastVLM-7B"
IMAGE_TOKEN_INDEX = -200  # what the model code looks for

# Load
tok = AutoTokenizer.from_pretrained(MID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MID,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
    trust_remote_code=True,
)

# Build chat -> render to string (not tokens) so we can place <image> exactly
messages = [
    {"role": "user", "content": "<image>\nDescribe this image in detail."}
]
rendered = tok.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)
pre, post = rendered.split("<image>", 1)

# Tokenize the text *around* the image token (no extra specials!)
pre_ids  = tok(pre,  return_tensors="pt", add_special_tokens=False).input_ids
post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids

# Splice in the IMAGE token id (-200) at the placeholder position
img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype)
input_ids = torch.cat([pre_ids, img_tok, post_ids], dim=1).to(model.device)
attention_mask = torch.ones_like(input_ids, device=model.device)

# Preprocess image via the model's own processor
img = Image.open("test-2.jpg").convert("RGB")
px = model.get_vision_tower().image_processor(images=img, return_tensors="pt")["pixel_values"]
px = px.to(model.device, dtype=model.dtype)

# Generate
with torch.no_grad():
    out = model.generate(
        inputs=input_ids,
        attention_mask=attention_mask,
        images=px,
        max_new_tokens=128,
    )
print(tok.decode(out[0], skip_special_tokens=True))

Citation

If you found this model useful, please cite the following paper:

@InProceedings{fastvlm2025,
  author = {Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari},
  title = {FastVLM: Efficient Vision Encoding for Vision Language Models},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2025},
}