Instructions to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047 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-C128-P1152-CTX2047 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-C128-P1152-CTX2047")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047 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-C128-P1152-CTX2047"
# 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-C128-P1152-CTX2047",
		"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-C128-P1152-CTX2047

SGLang

How to use AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047 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-C128-P1152-CTX2047" \
    --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-C128-P1152-CTX2047",
		"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-C128-P1152-CTX2047" \
        --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-C128-P1152-CTX2047",
		"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-C128-P1152-CTX2047 with Docker Model Runner:
```
docker model run hf.co/AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047
```

Qwen3.5-4B-AX650-GPTQ-Int4-C128-P1152-CTX2047 / qwen3_5_text_p128_l16_together.axmodel

lihongjie

update

3ad68d4 3 months ago

Download

History Blame Contribute Delete

84.9 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 171de7bce9bd633c7a562ff909cf109889deec3c133caccf07871d639e287ed4
Size of remote file:: 84.9 MB
SHA256:: 7b8a051aa42b16355b3967fcc5860f540c3d03248193527463d5b963008d4f8d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.