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 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
```

bugdaryan commited on Sep 15, 2023

Commit

ff48091

1 Parent(s): 90142a5

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -1

README.md CHANGED Viewed

@@ -99,4 +99,8 @@ prompt = f"Below is an instruction that describes a task, paired with an input t
 ans = pipe(prompt, max_new_tokens=200)
 print(ans[0]['generated_text'])
-```

 ans = pipe(prompt, max_new_tokens=200)
 print(ans[0]['generated_text'])
+```
+## Disclaimer
+WizardCoderSQL model follows the same license as WizardCoder. The content produced by any version of WizardCoderSQL is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.