Instructions to use QuantTrio/Step3-VL-10B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/Step3-VL-10B-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="QuantTrio/Step3-VL-10B-AWQ", 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 AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("QuantTrio/Step3-VL-10B-AWQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuantTrio/Step3-VL-10B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/Step3-VL-10B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/Step3-VL-10B-AWQ",
		"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/QuantTrio/Step3-VL-10B-AWQ

SGLang

How to use QuantTrio/Step3-VL-10B-AWQ 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 "QuantTrio/Step3-VL-10B-AWQ" \
    --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": "QuantTrio/Step3-VL-10B-AWQ",
		"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 "QuantTrio/Step3-VL-10B-AWQ" \
        --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": "QuantTrio/Step3-VL-10B-AWQ",
		"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 QuantTrio/Step3-VL-10B-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/Step3-VL-10B-AWQ
```

Step3-VL-10B-AWQ / config.json

JunHowie

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	{
	"name_or_path": "tclf90/Step3-VL-10B-AWQ",
	"architectures": [
	"StepVLForConditionalGeneration"
	],
	"auto_map": {
	"AutoConfig": "configuration_step_vl.StepRoboticsConfig",
	"AutoModelForCausalLM": "modeling_step_vl.Step3VL10BForCausalLM"
	},
	"model_type": "step_robotics",
	"im_end_token": "<im_end>",
	"im_patch_token": "<im_patch>",
	"im_start_token": "<im_start>",
	"image_token_len": 169,
	"patch_token_len": 81,
	"image_token_id": 151679,
	"understand_projector_stride": 2,
	"use_im_start_end": "true",
	"vision_select_layer": -1,
	"projector_bias": false,
	"vision_config": {
	"image_size": 728,
	"patch_size": 14,
	"width": 1536,
	"layers": 47,
	"heads": 16,
	"pool_type": "none",
	"output_dim": null,
	"use_cls_token": false,
	"ls_init_value": 0.1,
	"use_ln_post": false,
	"hidden_act": "quick_gelu"
	},
	"text_config": {
	"architectures": [
	"Qwen3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"eos_token_id": [
	151643,
	151645,
	151679
	],
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 12288,
	"max_position_embeddings": 40960,
	"max_window_layers": 36,
	"model_type": "qwen3",
	"num_attention_heads": 32,
	"num_hidden_layers": 36,
	"num_key_value_heads": 8,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 1000000,
	"sliding_window": null,
	"tie_word_embeddings": false,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.51.0",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 151936
	},
	"torch_dtype": "bfloat16",
	"quantization_config": {
	"quant_method": "awq",
	"bits": 4,
	"group_size": 128,
	"version": "gemm",
	"zero_point": true,
	"modules_to_not_convert": [
	"vision_model.transformer.resblocks.0.",
	"attn.qkv_proj",
	"attn.out_proj",
	"vit_large_projector",
	"model.layers.0."
	]
	}
	}