Instructions to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k with Transformers:

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

pipe = pipeline("image-text-to-text", model="AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k"
# 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/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k

SGLang

How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k 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/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k" \
    --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/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k",
		"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/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k" \
        --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/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k with Docker Model Runner:
```
docker model run hf.co/AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k
```

Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k

File size: 975 Bytes

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{
  "system_prompt": "you are a helpful assistant.",
  "model_name": "AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k",
  "url_tokenizer_model": "qwen3_5_tokenizer.txt",
  "tokenizer_type": "Qwen3_5VL",
  "post_config_path": "post_config.json",
  "template_filename_axmodel": "qwen3_5_text_p512_l%d_together.axmodel",
  "axmodel_num": 32,
  "full_attention_interval": 4,
  "filename_post_axmodel": "qwen3_5_text_post.axmodel",
  "filename_tokens_embed": "model.embed_tokens.weight.bfloat16.bin",
  "tokens_embed_num": 248320,
  "tokens_embed_size": 2560,
  "b_use_mmap_load_embed": true,
  "b_use_mmap_load_layer": true,
  "vlm_type": "Qwen3VL",
  "filename_image_encoder_axmodel": "qwen3_5_vision.axmodel",
  "vision_patch_size": 16,
  "vision_width": 384,
  "vision_height": 384,
  "vision_temporal_patch_size": 2,
  "vision_spatial_merge_size": 2,
  "vision_fps": 1,
  "vision_tokens_per_second": 1,
  "vision_cache_dir": "vision_cache",
  "devices": [
    0
  ]
}