Instructions to use hadeseus/HunyuanOCR-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hadeseus/HunyuanOCR-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="hadeseus/HunyuanOCR-mlx")
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 AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("hadeseus/HunyuanOCR-mlx", dtype="auto")

MLX

How to use hadeseus/HunyuanOCR-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("hadeseus/HunyuanOCR-mlx")
config = load_config("hadeseus/HunyuanOCR-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)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use hadeseus/HunyuanOCR-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hadeseus/HunyuanOCR-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": "hadeseus/HunyuanOCR-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/hadeseus/HunyuanOCR-mlx

SGLang

How to use hadeseus/HunyuanOCR-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 "hadeseus/HunyuanOCR-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": "hadeseus/HunyuanOCR-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 "hadeseus/HunyuanOCR-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": "hadeseus/HunyuanOCR-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 hadeseus/HunyuanOCR-mlx with Docker Model Runner:
```
docker model run hf.co/hadeseus/HunyuanOCR-mlx
```

HunyuanOCR-mlx / config.json

hadeseus

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	{
	"architectures": [
	"HunYuanVLForConditionalGeneration"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attention_head_dim": 128,
	"bos_token_id": 120000,
	"dtype": "bfloat16",
	"eod_token_id": 120020,
	"eos_token_id": [
	120007,
	120020
	],
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"image_end_token_id": 120119,
	"image_newline_token_id": 120121,
	"image_start_token_id": 120118,
	"image_token_id": 120120,
	"initializer_range": 0.02,
	"intermediate_size": 3584,
	"max_position_embeddings": 32768,
	"mlp_bias": false,
	"model_type": "hunyuan_vl",
	"norm_type": "rms",
	"num_attention_heads": 16,
	"num_experts": 1,
	"num_hidden_layers": 24,
	"num_key_value_heads": 8,
	"org_vocab_size": 120818,
	"pad_id": 120002,
	"pad_token_id": -1,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"alpha": 1000.0,
	"beta_fast": 32,
	"beta_slow": 1,
	"factor": 1.0,
	"mscale": 1.0,
	"mscale_all_dim": 1.0,
	"type": "xdrope",
	"xdrope_section": [
	16,
	16,
	16,
	16
	]
	},
	"rope_theta": 10000.0,
	"routed_scaling_factor": 1.0,
	"sep_token_id": 0,
	"text_end_id": 8,
	"text_start_id": 7,
	"tie_word_embeddings": true,
	"transformers_version": "4.49.0",
	"use_cache": true,
	"use_cla": false,
	"use_qk_norm": true,
	"vision_config": {
	"add_patchemb_bias": true,
	"attention_dropout": 0.0,
	"cat_extra_token": 1,
	"hidden_act": "gelu",
	"hidden_dropout": 0.0,
	"hidden_size": 1152,
	"img_max_token_num": 4096,
	"intermediate_size": 4304,
	"interpolate_mode": "bilinear",
	"max_image_size": 2048,
	"max_vit_seq_len": 16384,
	"num_attention_heads": 16,
	"num_channels": 3,
	"num_hidden_layers": 27,
	"out_hidden_size": 1024,
	"patch_size": 16,
	"rms_norm_eps": 1e-05,
	"spatial_merge_size": 2
	},
	"vocab_size": 120818
	}