Instructions to use SuanChang/rain-SQLCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SuanChang/rain-SQLCoder with Transformers:

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

pipe = pipeline("text-generation", model="SuanChang/rain-SQLCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SuanChang/rain-SQLCoder")
model = AutoModelForCausalLM.from_pretrained("SuanChang/rain-SQLCoder", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SuanChang/rain-SQLCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SuanChang/rain-SQLCoder

SGLang

How to use SuanChang/rain-SQLCoder 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 "SuanChang/rain-SQLCoder" \
    --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": "SuanChang/rain-SQLCoder",
		"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 "SuanChang/rain-SQLCoder" \
        --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": "SuanChang/rain-SQLCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SuanChang/rain-SQLCoder with Docker Model Runner:
```
docker model run hf.co/SuanChang/rain-SQLCoder
```

suan-chang commited on Mar 10, 2025

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@@ -1,3 +1,10 @@
 # Introduction
 [Rain's SQLCoder](https://huggingface.co/SuanChang/rain-SQLCoder) is a state-of-the-art large language model (LLM) designed for natural language-to-SparkSQL generation. Rain's SQLCoder, with 32B parameters, is fine-tuned from the [Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct). Optimized for natural language-to-SparkSQL conversion tasks, Rain's SQLCoder effectively handles contexts of up to 32k tokens, making it particularly suitable for generating complex and large-scale SQL queries.

+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-Coder-32B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+---
 # Introduction
 [Rain's SQLCoder](https://huggingface.co/SuanChang/rain-SQLCoder) is a state-of-the-art large language model (LLM) designed for natural language-to-SparkSQL generation. Rain's SQLCoder, with 32B parameters, is fine-tuned from the [Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct). Optimized for natural language-to-SparkSQL conversion tasks, Rain's SQLCoder effectively handles contexts of up to 32k tokens, making it particularly suitable for generating complex and large-scale SQL queries.