Image-Text-to-Text
Transformers
English
Chinese
Qwen3-VL
Qwen3-VL-2B-Instruct
Qwen3-VL-4B-Instruct
Int4
VLM
GPTQ
AX630C
axllm
Instructions to use AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448
- SGLang
How to use AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448 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 "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448 with Docker Model Runner:
docker model run hf.co/AXERA-TECH/Qwen3-VL-2B-Instruct-GPTQ-Int4-AX630C-P320-CTX448
- Xet hash:
- a6149a277f9baa51c233c051bc5ee5db4f65db85c49d77bd21081c5cc9a973c4
- Size of remote file:
- 40.1 MB
- SHA256:
- cabf7ef2c761b260bccbf3707121f2fc8da11a11c19e161468533b77b3577782
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