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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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

File size: 564 Bytes

0122ccb

{
  "additional_special_tokens": [
    "<|endoftext|>",
    "<fim_prefix>",
    "<fim_middle>",
    "<fim_suffix>",
    "<fim_pad>",
    "<filename>",
    "<gh_stars>",
    "<issue_start>",
    "<issue_comment>",
    "<issue_closed>",
    "<jupyter_start>",
    "<jupyter_text>",
    "<jupyter_code>",
    "<jupyter_output>",
    "<empty_output>",
    "<commit_before>",
    "<commit_msg>",
    "<commit_after>",
    "<reponame>"
  ],
  "bos_token": "<|endoftext|>",
  "eos_token": "<|endoftext|>",
  "pad_token": "<|endoftext|>",
  "unk_token": "<|endoftext|>"
}