Instructions to use bugdaryan/WizardCoderSQL-15B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bugdaryan/WizardCoderSQL-15B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="bugdaryan/WizardCoderSQL-15B-V1.0")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bugdaryan/WizardCoderSQL-15B-V1.0")
model = AutoModelForCausalLM.from_pretrained("bugdaryan/WizardCoderSQL-15B-V1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bugdaryan/WizardCoderSQL-15B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bugdaryan/WizardCoderSQL-15B-V1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bugdaryan/WizardCoderSQL-15B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bugdaryan/WizardCoderSQL-15B-V1.0

SGLang

How to use bugdaryan/WizardCoderSQL-15B-V1.0 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 "bugdaryan/WizardCoderSQL-15B-V1.0" \
    --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": "bugdaryan/WizardCoderSQL-15B-V1.0",
		"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 "bugdaryan/WizardCoderSQL-15B-V1.0" \
        --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": "bugdaryan/WizardCoderSQL-15B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bugdaryan/WizardCoderSQL-15B-V1.0 with Docker Model Runner:
```
docker model run hf.co/bugdaryan/WizardCoderSQL-15B-V1.0
```

WizardCoderSQL-15B-V1.0

Commit History

Apr 3, 2025

07:27
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
752ea89

Sep 15, 2023

15:33
Update README.md

bugdaryan

c5ffc08
15:22
Update README.md

bugdaryan

85488c5
15:20
Update README.md

bugdaryan

ff48091
15:18
Update README.md

bugdaryan

90142a5
15:17
Update README.md

bugdaryan

835e6cc
15:17
Update README.md

bugdaryan

c99b067
15:16
Update README.md

bugdaryan

dfad5b9
15:15
Update README.md

bugdaryan

fb40f7e

Sep 11, 2023

10:27
Create README.md

bugdaryan

2326234

Sep 8, 2023

21:26
Upload tokenizer

bugdaryan

0122ccb
21:26
Upload GPTBigCodeForCausalLM

bugdaryan

5ee0b67
21:04
initial commit

bugdaryan

126f87a

Commit History

Apr 3, 2025

Adding `safetensors` variant of this model⁠

Sep 15, 2023

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Update README.md

Sep 11, 2023

Create README.md

Sep 8, 2023

Upload tokenizer

Upload GPTBigCodeForCausalLM

initial commit