Instructions to use dbaek111/fastvlm-1.5b-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dbaek111/fastvlm-1.5b-mlx-q4 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("dbaek111/fastvlm-1.5b-mlx-q4")
config = load_config("dbaek111/fastvlm-1.5b-mlx-q4")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Transformers

How to use dbaek111/fastvlm-1.5b-mlx-q4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="dbaek111/fastvlm-1.5b-mlx-q4", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dbaek111/fastvlm-1.5b-mlx-q4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use dbaek111/fastvlm-1.5b-mlx-q4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dbaek111/fastvlm-1.5b-mlx-q4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbaek111/fastvlm-1.5b-mlx-q4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/dbaek111/fastvlm-1.5b-mlx-q4

SGLang

How to use dbaek111/fastvlm-1.5b-mlx-q4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dbaek111/fastvlm-1.5b-mlx-q4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbaek111/fastvlm-1.5b-mlx-q4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dbaek111/fastvlm-1.5b-mlx-q4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbaek111/fastvlm-1.5b-mlx-q4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use dbaek111/fastvlm-1.5b-mlx-q4 with Docker Model Runner:
```
docker model run hf.co/dbaek111/fastvlm-1.5b-mlx-q4
```

fastvlm-1.5b-mlx-q4

File size: 1,474 Bytes

834483b

---
license: apple-amlr
library_name: mlx-vlm
base_model: apple/FastVLM-1.5B
tags:
- fastvlm
- mlx
- mlx-vlm
- vlm
- quantized
- apple-silicon
- 1.5B
- q4 / 4-bit quantized
---

# fastvlm-1.5b-mlx-q4

This repository contains an MLX-converted FastVLM checkpoint.

## Model

- Base model: `apple/FastVLM-1.5B`
- Parameters: `1.5B`
- Precision: `q4 / 4-bit quantized`
- Approx. folder size: `1.4G`

The checkpoint was converted from Apple FastVLM using the official FastVLM model export workflow and patched `mlx-vlm`.

## Files

This repository should include:

- `config.json`
- MLX model weights
- tokenizer files
- `fastvithd.mlpackage` vision tower

## Example Usage

```bash
hf download dbaek111/fastvlm-1.5b-mlx-q4 --local-dir ./fastvlm-1.5b-mlx-q4

python -m mlx_vlm.generate \
  --model ./fastvlm-1.5b-mlx-q4 \
  --image /path/to/your/image.jpg \
  --prompt "Explain the image." \
  --max-tokens 64 \
  --temp 0.0
```

## Benchmark

Benchmark condition:

- Images: three 512px test images
- Max tokens: `64`
- Temperature: `0.0`
- Same prompt across all tested variants
- Model loaded once, then images processed sequentially

| Model | Size | Load | Img1 | Img2 | Img3 | Avg |
|---|---:|---:|---:|---:|---:|---:|
| fastvlm-1.5b-mlx-q4 | 1.4G | 2.46s | 0.454s | 0.462s | 0.443s | 0.453s |

## Notes

This is a converted and quantized derivative of Apple FastVLM.

Please refer to the original Apple FastVLM repository and model card for license and usage conditions.