Instructions to use AXERA-TECH/Qwen3-1.7B-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/Qwen3-1.7B-GPTQ-Int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AXERA-TECH/Qwen3-1.7B-GPTQ-Int4")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/Qwen3-1.7B-GPTQ-Int4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXERA-TECH/Qwen3-1.7B-GPTQ-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXERA-TECH/Qwen3-1.7B-GPTQ-Int4" # 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/Qwen3-1.7B-GPTQ-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AXERA-TECH/Qwen3-1.7B-GPTQ-Int4
- SGLang
How to use AXERA-TECH/Qwen3-1.7B-GPTQ-Int4 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/Qwen3-1.7B-GPTQ-Int4" \ --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/Qwen3-1.7B-GPTQ-Int4", "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/Qwen3-1.7B-GPTQ-Int4" \ --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/Qwen3-1.7B-GPTQ-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AXERA-TECH/Qwen3-1.7B-GPTQ-Int4 with Docker Model Runner:
docker model run hf.co/AXERA-TECH/Qwen3-1.7B-GPTQ-Int4
Download model.embed_tokens.weight.bfloat16.bin from AXERA-TECH/Qwen3-1.7B-GPTQ-Int4: direct link, hf CLI and curl.
- Browser
- Download file 622 MB
-
https://huggingface.co/AXERA-TECH/Qwen3-1.7B-GPTQ-Int4/resolve/main/model.embed_tokens.weight.bfloat16.bin
- Command line
-
hf download hf://AXERA-TECH/Qwen3-1.7B-GPTQ-Int4/model.embed_tokens.weight.bfloat16.bin
-
curl -L -o model.embed_tokens.weight.bfloat16.bin https://huggingface.co/AXERA-TECH/Qwen3-1.7B-GPTQ-Int4/resolve/main/model.embed_tokens.weight.bfloat16.bin
622 MB
- Xet hash:
- 2537e5d3f586e3dad3645dabd8c2228fc9f48aed5d83d51983a8a239ea69132f
- Size of remote file:
- 622 MB
- SHA256:
- f56b71b6939a944afce2902799810e2acfaefbf0dbcacd8cda1add4eb710f05d
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