Instructions to use AXERA-TECH/Qwen3-VL-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AXERA-TECH/Qwen3-VL-4B-Instruct 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-4B-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AXERA-TECH/Qwen3-VL-4B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AXERA-TECH/Qwen3-VL-4B-Instruct 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-4B-Instruct"
# 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-4B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AXERA-TECH/Qwen3-VL-4B-Instruct

SGLang

How to use AXERA-TECH/Qwen3-VL-4B-Instruct 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-4B-Instruct" \
    --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-4B-Instruct",
		"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-4B-Instruct" \
        --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-4B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AXERA-TECH/Qwen3-VL-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/AXERA-TECH/Qwen3-VL-4B-Instruct
```

Qwen3-VL-4B-Instruct / config.json

sunyijie

chore: remove unnecessary files and clean up project structure

c84d9ab 17 days ago

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	{
	"system_prompt": "You are a helpful assistant.",
	"model_name": "Qwen3-VL-4B-Instruct-AX650-c128_p1152",
	"url_tokenizer_model": "tokenizer.txt",
	"tokenizer_type": "Qwen3VL",
	"vlm_type": "Qwen3VL",
	"post_config_path": "post_config.json",
	"template_filename_axmodel": "qwen3_vl_text_p128_l%d_together.axmodel",
	"axmodel_num": 28,
	"filename_post_axmodel": "qwen3_vl_text_post.axmodel",
	"filename_image_encoder_axmodel": "Qwen3-VL-4B-Instruct_vision.axmodel",
	"filename_tokens_embed": "model.embed_tokens.weight.bfloat16.bin",
	"tokens_embed_num": 151936,
	"tokens_embed_size": 2048,
	"hidden_size": 2048,
	"head_dim": 128,
	"num_attention_heads": 16,
	"num_key_value_heads": 8,
	"rms_norm_eps": 1e-06,
	"bos_token_id": 151643,
	"eos_token_id": 151645,
	"vision_width": 384,
	"vision_height": 384,
	"vision_patch_size": 16,
	"vision_temporal_patch_size": 2,
	"vision_spatial_merge_size": 2,
	"use_mmap_load_embed": true,
	"use_mmap_load_layer": true
	}