Image-Text-to-Text
PEFT
Safetensors
Transformers
English
lora
spatial-reasoning
visual-question-answering
chain-of-thought
conversational
Instructions to use spatialchain/Qwen3-VL-4B-Thinking-SpatialChain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use spatialchain/Qwen3-VL-4B-Thinking-SpatialChain with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Thinking") model = PeftModel.from_pretrained(base_model, "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain") - Transformers
How to use spatialchain/Qwen3-VL-4B-Thinking-SpatialChain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="spatialchain/Qwen3-VL-4B-Thinking-SpatialChain") 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 AutoModel model = AutoModel.from_pretrained("spatialchain/Qwen3-VL-4B-Thinking-SpatialChain", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use spatialchain/Qwen3-VL-4B-Thinking-SpatialChain with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain", "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/spatialchain/Qwen3-VL-4B-Thinking-SpatialChain
- SGLang
How to use spatialchain/Qwen3-VL-4B-Thinking-SpatialChain 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 "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain" \ --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": "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain", "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 "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain" \ --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": "spatialchain/Qwen3-VL-4B-Thinking-SpatialChain", "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 spatialchain/Qwen3-VL-4B-Thinking-SpatialChain with Docker Model Runner:
docker model run hf.co/spatialchain/Qwen3-VL-4B-Thinking-SpatialChain
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "Qwen/Qwen3-VL-4B-Thinking", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_bias": false, | |
| "lora_dropout": 0.05, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": [ | |
| "lm_head", | |
| "embed_tokens" | |
| ], | |
| "peft_type": "LORA", | |
| "peft_version": "0.19.1", | |
| "qalora_group_size": 16, | |
| "r": 32, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "up_proj", | |
| "linear_fc1", | |
| "down_proj", | |
| "qkv", | |
| "o_proj", | |
| "k_proj", | |
| "proj", | |
| "v_proj", | |
| "linear_fc2", | |
| "q_proj", | |
| "gate_proj" | |
| ], | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": true | |
| } |