Instructions to use EZCon/FastVLM-1.5B-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EZCon/FastVLM-1.5B-8bit-mlx 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("EZCon/FastVLM-1.5B-8bit-mlx")
config = load_config("EZCon/FastVLM-1.5B-8bit-mlx")

# 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 EZCon/FastVLM-1.5B-8bit-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="EZCon/FastVLM-1.5B-8bit-mlx", 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("EZCon/FastVLM-1.5B-8bit-mlx", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use EZCon/FastVLM-1.5B-8bit-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EZCon/FastVLM-1.5B-8bit-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EZCon/FastVLM-1.5B-8bit-mlx",
		"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/EZCon/FastVLM-1.5B-8bit-mlx

SGLang

How to use EZCon/FastVLM-1.5B-8bit-mlx 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 "EZCon/FastVLM-1.5B-8bit-mlx" \
    --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": "EZCon/FastVLM-1.5B-8bit-mlx",
		"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 "EZCon/FastVLM-1.5B-8bit-mlx" \
        --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": "EZCon/FastVLM-1.5B-8bit-mlx",
		"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 EZCon/FastVLM-1.5B-8bit-mlx with Docker Model Runner:
```
docker model run hf.co/EZCon/FastVLM-1.5B-8bit-mlx
```

FastVLM-1.5B-8bit-mlx

File size: 1,660 Bytes

f9222eb

{
    "architectures": [
        "LlavaQwen2ForCausalLM"
    ],
    "attention_dropout": 0.0,
    "auto_map": {
        "AutoConfig": "llava_qwen.LlavaConfig",
        "AutoModelForCausalLM": "llava_qwen.LlavaQwen2ForCausalLM"
    },
    "bos_token_id": 151643,
    "eos_token_id": 151645,
    "freeze_mm_mlp_adapter": false,
    "hidden_act": "silu",
    "hidden_size": 1536,
    "image_aspect_ratio": "pad",
    "image_grid_pinpoints": null,
    "initializer_range": 0.02,
    "intermediate_size": 8960,
    "max_position_embeddings": 32768,
    "max_window_layers": 28,
    "mm_hidden_size": 3072,
    "mm_patch_merge_type": "flat",
    "mm_projector_lr": null,
    "mm_projector_type": "mlp2x_gelu",
    "mm_use_im_patch_token": false,
    "mm_use_im_start_end": false,
    "mm_vision_select_feature": "patch",
    "mm_vision_select_layer": -2,
    "mm_vision_tower": "mobileclip_l_1024",
    "model_type": "llava_qwen2",
    "num_attention_heads": 12,
    "num_hidden_layers": 28,
    "num_key_value_heads": 2,
    "quantization": {
        "group_size": 64,
        "bits": 8,
        "mode": "affine"
    },
    "quantization_config": {
        "group_size": 64,
        "bits": 8,
        "mode": "affine"
    },
    "rms_norm_eps": 1e-06,
    "rope_theta": 1000000.0,
    "sliding_window": 32768,
    "tie_word_embeddings": true,
    "tokenizer_model_max_length": 8192,
    "tokenizer_padding_side": "right",
    "transformers_version": "4.39.3",
    "tune_mm_mlp_adapter": false,
    "unfreeze_mm_vision_tower": true,
    "use_cache": true,
    "use_mm_proj": true,
    "use_sliding_window": false,
    "vision_config": {},
    "vocab_size": 151936
}