Instructions to use drawais/Qwen3-Reranker-4B-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drawais/Qwen3-Reranker-4B-AWQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="drawais/Qwen3-Reranker-4B-AWQ-INT4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drawais/Qwen3-Reranker-4B-AWQ-INT4")
model = AutoModelForCausalLM.from_pretrained("drawais/Qwen3-Reranker-4B-AWQ-INT4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use drawais/Qwen3-Reranker-4B-AWQ-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drawais/Qwen3-Reranker-4B-AWQ-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drawais/Qwen3-Reranker-4B-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drawais/Qwen3-Reranker-4B-AWQ-INT4

SGLang

How to use drawais/Qwen3-Reranker-4B-AWQ-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 "drawais/Qwen3-Reranker-4B-AWQ-INT4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drawais/Qwen3-Reranker-4B-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "drawais/Qwen3-Reranker-4B-AWQ-INT4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drawais/Qwen3-Reranker-4B-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drawais/Qwen3-Reranker-4B-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/drawais/Qwen3-Reranker-4B-AWQ-INT4
```

Qwen3-Reranker-4B-AWQ-INT4 / README.md

drawais

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	---
	license: apache-2.0

	base_model: Qwen/Qwen3-Reranker-4B
	tags:
	- quantized
	- 4-bit
	- int4
	- awq
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Qwen3-Reranker-4B-AWQ-INT4

	INT4 weight-only quantization of [`Qwen/Qwen3-Reranker-4B`](https://huggingface.co/Qwen/Qwen3-Reranker-4B).

	Qwen 3 Reranker 4B in INT4. Pair with the Embedding kit for a fully local retrieval stack.

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| [Qwen/Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) \|
	\| Quantization \| INT4 weight-only \|
	\| Approx. on-disk size \| ~2.7 GB \|
	\| Languages \| English \|

	## Load (vLLM)

	```bash
	vllm serve drawais/Qwen3-Reranker-4B-AWQ-INT4 \
	--max-model-len 32768 \
	--gpu-memory-utilization 0.94
	```

	```python
	from vllm import LLM, SamplingParams
	llm = LLM(model="drawais/Qwen3-Reranker-4B-AWQ-INT4", max_model_len=32768)
	print(llm.generate(["Hello!"], SamplingParams(max_tokens=128))[0].outputs[0].text)
	```

	## Footprint

	~2.7 GB on disk. Recommended VRAM: enough headroom for KV cache.

	## License & attribution

	This artifact is a derivative work of [`Qwen/Qwen3-Reranker-4B`](https://huggingface.co/Qwen/Qwen3-Reranker-4B),
	released by its original authors under the Apache License, Version 2.0.

	This artifact is distributed under the same license. The full license text is
	included in [`LICENSE`](LICENSE), and required attribution is in [`NOTICE`](NOTICE).

	License text: https://www.apache.org/licenses/LICENSE-2.0
	Source model: https://huggingface.co/Qwen/Qwen3-Reranker-4B