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

Libraries

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

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

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

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

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-Python-13B-V1.0")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-Python-13B-V1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

WizardCoder-Python-13B-V1.0

Commit History

Nov 14, 2024

14:36
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
7f16c1b

Jan 19, 2024

03:06
Update README.md (#8)⁠

WizardLM +1: Ziyang

Verified signature
5ac6748

Sep 9, 2023

06:43
Update README.md

WizardLM

d920d26

Aug 31, 2023

08:09
update 7B result (#4)⁠

WizardLM +1: Ziyang

b3bbc22

Aug 29, 2023

02:07
Update README.md

WizardLM

f92da49

Aug 28, 2023

02:19
Update README.md (#3)⁠

WizardLM +1: Ziyang

4000a99

Aug 27, 2023

15:04
Update README.md (#2)⁠

WizardLM +1: Ziyang

8db434a
15:03
Update README.md (#1)⁠

WizardLM +1: haipeng1

95a7439
14:17
initial release

Ziyang

219756e
13:40
Update README.md

Ziyang

06e9db0
13:39
Update README.md

Ziyang

aa20f3d

Aug 26, 2023

04:59
initial commit

WizardLM

de1f14b

Commit History

Nov 14, 2024

Adding `safetensors` variant of this model⁠

Jan 19, 2024

Update README.md (#8)⁠

Sep 9, 2023

Update README.md

Aug 31, 2023

update 7B result (#4)⁠

Aug 29, 2023

Update README.md

Aug 28, 2023

Update README.md (#3)⁠

Aug 27, 2023

Update README.md (#2)⁠

Update README.md (#1)⁠

initial release

Update README.md

Update README.md

Aug 26, 2023

initial commit