Instructions to use Kwai-AutoSQL/Kwai-AutoSQL-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kwai-AutoSQL/Kwai-AutoSQL-32B with Transformers:

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

pipe = pipeline("text-generation", model="Kwai-AutoSQL/Kwai-AutoSQL-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kwai-AutoSQL/Kwai-AutoSQL-32B")
model = AutoModelForCausalLM.from_pretrained("Kwai-AutoSQL/Kwai-AutoSQL-32B")
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 Settings

vLLM

How to use Kwai-AutoSQL/Kwai-AutoSQL-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kwai-AutoSQL/Kwai-AutoSQL-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kwai-AutoSQL/Kwai-AutoSQL-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kwai-AutoSQL/Kwai-AutoSQL-32B

SGLang

How to use Kwai-AutoSQL/Kwai-AutoSQL-32B 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 "Kwai-AutoSQL/Kwai-AutoSQL-32B" \
    --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": "Kwai-AutoSQL/Kwai-AutoSQL-32B",
		"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 "Kwai-AutoSQL/Kwai-AutoSQL-32B" \
        --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": "Kwai-AutoSQL/Kwai-AutoSQL-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kwai-AutoSQL/Kwai-AutoSQL-32B with Docker Model Runner:
```
docker model run hf.co/Kwai-AutoSQL/Kwai-AutoSQL-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kwai-AutoSQL-32B

This repository only contains fine-tuned model weights, without training and inference scripts.

Base Model

Original base model: Qwen/Qwen3-32B The base model is released under Apache License 2.0.

License Statement

All model weights, config.json, tokenizer files and all model binary assets in this repo are derivative works of Qwen/Qwen3-32B, governed by Apache License 2.0. Copyright 2024 Alibaba Cloud
Repository descriptive documentation (README.md) and the root LICENSE file are licensed under MIT License. Full MIT license text can be found in the root LICENSE file. Copyright (c) 2026 Kuaishou Business Team

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Safetensors

Model size

33B params

Tensor type

BF16

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Quantizations

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