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

Benchmark	FastVLM-0.5B	FastVLM-1.5B	FastVLM-7B
Ai2D	68.0	77.4	83.6
ScienceQA	85.2	94.4	96.7
MMMU	33.9	37.8	45.4
VQAv2	76.3	79.1	80.8
ChartQA	76.0	80.1	85.0
TextVQA	64.5	70.4	74.9
InfoVQA	46.4	59.7	75.8
DocVQA	82.5	88.3	93.2
OCRBench	63.9	70.2	73.1
RealWorldQA	56.1	61.2	67.2
SeedBench-Img	71.0	74.2	75.4

Usage Example

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

Download the model

huggingface-cli download apple/FastVLM-0.5B

Run inference using predict.py from the official repo.

bash

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:

python

import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM

MID = "apple/FastVLM-0.5B"
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},
}

Prathat/FastVLM-0.5B

FastVLM: Efficient Vision Encoding for Vision Language Models

Highlights

Evaluations

Usage Example

Run inference with Transformers (Remote Code)

Citation

Prathat/
FastVLM-0.5B