Instructions to use vanillaOVO/WizardCoder-Python-13B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanillaOVO/WizardCoder-Python-13B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="vanillaOVO/WizardCoder-Python-13B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/WizardCoder-Python-13B-V1.0")
model = AutoModelForCausalLM.from_pretrained("vanillaOVO/WizardCoder-Python-13B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vanillaOVO/WizardCoder-Python-13B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanillaOVO/WizardCoder-Python-13B-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": "vanillaOVO/WizardCoder-Python-13B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vanillaOVO/WizardCoder-Python-13B-V1.0

SGLang

How to use vanillaOVO/WizardCoder-Python-13B-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 "vanillaOVO/WizardCoder-Python-13B-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": "vanillaOVO/WizardCoder-Python-13B-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 "vanillaOVO/WizardCoder-Python-13B-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": "vanillaOVO/WizardCoder-Python-13B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vanillaOVO/WizardCoder-Python-13B-V1.0 with Docker Model Runner:
```
docker model run hf.co/vanillaOVO/WizardCoder-Python-13B-V1.0
```

vanillaOVO commited on Jun 20, 2024

Commit

0e9c1f8

verified ·

1 Parent(s): 83d4cbe

Update README.md

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@@ -20,6 +20,10 @@ model-index:
       verified: false
 ---
 <p align="center">
 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>  • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>    • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
 </p>
@@ -27,6 +31,7 @@ model-index:
     👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
 </p>
 ## News
 - 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).

       verified: false
 ---
+## Note
+This is a replica of the official repository, intended solely for research purposes to replicate results. If there are any copyright issues, please contact me.
 <p align="center">
 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>  • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>    • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
 </p>
     👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
 </p>
 ## News
 - 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).