Image-Text-to-Text
Transformers
Safetensors
step_robotics
text-generation
vLLM
AWQ
conversational
custom_code
4-bit precision
awq
Instructions to use QuantTrio/Step3-VL-10B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| from typing import Any, Optional, Union | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers import Qwen3Config | |
| class StepRoboticsVisionEncoderConfig(PretrainedConfig): | |
| def __init__( | |
| self, | |
| width=1536, | |
| layers=47, | |
| heads=16, | |
| num_channels=3, | |
| image_size=728, | |
| mlp_ratio = 8960/1536, | |
| patch_size=14, | |
| hidden_act="quick_gelu", | |
| layer_norm_eps=1e-5, | |
| ues_cls_token=False, | |
| use_ln_pre=True, | |
| use_ln_post=False, | |
| use_abs_posemb=True, | |
| use_rope2d=True, | |
| ls_init_value=0.1, | |
| **kwargs, | |
| ): | |
| self.width = width | |
| self.layers = layers | |
| self.heads = heads | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.mlp_ratio = mlp_ratio | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_act = hidden_act | |
| self.ues_cls_token = ues_cls_token | |
| self.use_ln_pre = use_ln_pre | |
| self.ls_init_value = ls_init_value | |
| self.use_ln_post = use_ln_post | |
| self.use_abs_posemb = use_abs_posemb | |
| self.use_rope2d = use_rope2d | |
| super().__init__(**kwargs) | |
| class StepRoboticsConfig(PretrainedConfig): | |
| model_type = "step_robotics" | |
| architectures = ["StepVLForConditionalGeneration"] | |
| def __init__( | |
| self, | |
| vision_config: Optional[Union[dict, StepRoboticsVisionEncoderConfig]] = None, | |
| text_config: Optional[Union[dict, Qwen3Config]] = None, | |
| understand_projector_stride: int = 2, | |
| projector_bias: bool = False, | |
| image_token_id: int = 151679, | |
| **kwargs, | |
| ) -> None: | |
| if vision_config is None: | |
| vision_config = StepRoboticsVisionEncoderConfig() | |
| elif isinstance(vision_config, dict): | |
| vision_config = StepRoboticsVisionEncoderConfig(**vision_config) | |
| self.vision_config = vision_config | |
| if text_config is None: | |
| text_config = Qwen3Config() | |
| elif isinstance(text_config, dict): | |
| text_config = Qwen3Config(**text_config) | |
| self.text_config = text_config | |
| self.understand_projector_stride = understand_projector_stride | |
| self.projector_bias = projector_bias | |
| self.hidden_size = text_config.hidden_size | |
| self.image_token_id = image_token_id | |
| # Help Auto classes find the correct implementation when saving/loading. | |
| super().__init__(**kwargs) | |