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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Update README.md

by Ziyang - opened Aug 27, 2023

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-6

Files changed (1) hide show

README.md +3 -6

README.md CHANGED Viewed

@@ -74,18 +74,15 @@ model-index:
 ## Prompt Format
 ```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-{instruction}
-### Response:
 ```
 ## Inference Demo Script
 We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
 ## Citation
 Please cite the repo if you use the data or code in this repo.

 ## Prompt Format
 ```
+"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
 ```
 ## Inference Demo Script
 We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
+Note: This script supports `WizardLM/WizardCoder-Python-34B/13B/7B-V1.0`. If you want to inference with `WizardLM/WizardCoder-15B/3B/1B-V1.0`, please change the `stop_tokens = ['</s>']` to `stop_tokens = ['<|endoftext|>']` in the script.
 ## Citation
 Please cite the repo if you use the data or code in this repo.