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
```

dbaek111 commited on 15 days ago

Commit

89353f5

verified ·

1 Parent(s): 1f8d546

Update README

Browse files

Files changed (1) hide show

README.md +69 -0

README.md ADDED Viewed

	@@ -0,0 +1,69 @@

+---
+license: apple-amlr
+library_name: mlx-vlm
+base_model: apple/FastVLM-7B
+tags:
+- fastvlm
+- mlx
+- mlx-vlm
+- vlm
+- quantized
+- apple-silicon
+- 7B
+- q4 / 4-bit quantized
+---
+# 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
+```bash
+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
+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-7b-mlx-q4 | 4.9G | 3.00s | 1.139s | 1.303s | 1.277s | 1.239s |
+## 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.