Instructions to use dbaek111/fastvlm-7b-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dbaek111/fastvlm-7b-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-7b-mlx-q4")
config = load_config("dbaek111/fastvlm-7b-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-7b-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-7b-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-7b-mlx-q4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use dbaek111/fastvlm-7b-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-7b-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-7b-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-7b-mlx-q4

SGLang

How to use dbaek111/fastvlm-7b-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-7b-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-7b-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-7b-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-7b-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"
						}
					}
				]
			}
		]
	}'

How to use dbaek111/fastvlm-7b-mlx-q4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dbaek111/fastvlm-7b-mlx-q4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dbaek111/fastvlm-7b-mlx-q4 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dbaek111/fastvlm-7b-mlx-q4

Run Hermes

hermes

OpenClaw new

How to use dbaek111/fastvlm-7b-mlx-q4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dbaek111/fastvlm-7b-mlx-q4"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dbaek111/fastvlm-7b-mlx-q4" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use dbaek111/fastvlm-7b-mlx-q4 with Docker Model Runner:
```
docker model run hf.co/dbaek111/fastvlm-7b-mlx-q4
```

fastvlm-7b-mlx-q4 / README.md

dbaek111

Update benchmark tables with 512px and 1024px comparison

babd32c verified 10 days ago

3.99 kB

license: apple-amlr
library_name: mlx
pipeline_tag: image-text-to-text
base_model: apple/FastVLM-7B
base_model_relation: quantized
tags:
  - fastvlm
  - mlx
  - mlx-vlm
  - safetensors
  - transformers
  - llava_qwen2
  - image-text-to-text
  - text-generation
  - conversational
  - custom_code
  - vision-language
  - multimodal
  - apple-silicon
  - quantized
  - 7B
  - q4 / 4-bit quantized
  - 4-bit
  - 4-bit precision
  - q4

fastvlm-7b-mlx-q4

This repository contains an MLX-converted FastVLM checkpoint.

Model

Base model: apple/FastVLM-7B
Parameters: 7B
Precision: q4 / 4-bit quantized
Approx. folder size: 4.9G

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

hf download dbaek111/fastvlm-7b-mlx-q4 --local-dir ./fastvlm-7b-mlx-q4

python -m mlx_vlm.generate \
  --model ./fastvlm-7b-mlx-q4 \
  --image /path/to/your/image.jpg \
  --prompt "Explain the image." \
  --max-tokens 64 \
  --temp 0.0

Benchmark

Benchmarked on an Apple Silicon Mac with the patched FastVLM mlx-vlm workflow.

Task: pedestrian wayfinding captioning
Images: three local test images resized to 512px and 1024px long edge
Prompt: Describe what is visible for pedestrian wayfinding in one short sentence. Do not list categories. Do not mention anything you cannot see. Keep under 30 words.
Max tokens: 64
Temperature: 0.0
Timing: model loaded once per image set, then three images processed sequentially

Model Selection

Model	Size	Precision	Avg 512px	Avg 1024px	Load	Recommended use
fastvlm-0.5b-mlx-q4	819M	q4	0.338s	0.370s	2.82s	Smallest and fastest; rough real-time captions
fastvlm-0.5b-mlx-q8	1.1G	q8	0.419s	0.414s	2.57s	Fast, with richer captions than 0.5B q4
fastvlm-0.5b-mlx-fp16	1.6G	fp16	0.435s	0.421s	2.79s	Small FP16 baseline
fastvlm-1.5b-mlx-q4	1.4G	q4	0.447s	0.464s	2.65s	Best real-time balance for pedestrian wayfinding
fastvlm-1.5b-mlx-q8	2.2G	q8	0.552s	0.541s	2.68s	More detail while staying sub-second
fastvlm-1.5b-mlx-fp16	3.8G	fp16	0.636s	0.557s	3.08s	1.5B FP16 reference variant
fastvlm-7b-mlx-q4	4.9G	q4	1.263s	1.241s	3.04s	Best quality/latency tradeoff among 7B variants
fastvlm-7b-mlx-q8	8.0G	q8	1.497s	1.495s	3.85s	Higher precision 7B, slower than q4
fastvlm-7b-mlx-fp16	15G	fp16	1.834s	1.874s	45.48s	Full precision reference; expensive to load

Per-Image Timing

Each cell is img1 / img2 / img3 inference time in seconds.

Model	512px images	1024px images
fastvlm-0.5b-mlx-q4	0.318 / 0.353 / 0.343	0.374 / 0.370 / 0.366
fastvlm-0.5b-mlx-q8	0.393 / 0.465 / 0.400	0.424 / 0.387 / 0.431
fastvlm-0.5b-mlx-fp16	0.394 / 0.490 / 0.421	0.430 / 0.395 / 0.437
fastvlm-1.5b-mlx-q4	0.454 / 0.458 / 0.430	0.465 / 0.472 / 0.456
fastvlm-1.5b-mlx-q8	0.591 / 0.611 / 0.453	0.594 / 0.552 / 0.477
fastvlm-1.5b-mlx-fp16	0.701 / 0.709 / 0.496	0.520 / 0.635 / 0.516
fastvlm-7b-mlx-q4	1.161 / 1.330 / 1.297	1.081 / 1.343 / 1.298
fastvlm-7b-mlx-q8	1.364 / 1.639 / 1.487	1.341 / 1.712 / 1.432
fastvlm-7b-mlx-fp16	1.561 / 2.041 / 1.902	1.595 / 2.277 / 1.749

Compatibility

This is an MLX export of FastVLM for Apple Silicon Macs. It includes the CoreML FastViTHD vision tower as fastvithd.mlpackage.

This repository is not a standard PyTorch Transformers checkpoint and is not intended for vLLM, SGLang, or Linux GPU inference.

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.