Instructions to use FelixChao/WizardDolphin-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FelixChao/WizardDolphin-7B with Transformers:

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

pipe = pipeline("text-generation", model="FelixChao/WizardDolphin-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FelixChao/WizardDolphin-7B")
model = AutoModelForCausalLM.from_pretrained("FelixChao/WizardDolphin-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FelixChao/WizardDolphin-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FelixChao/WizardDolphin-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FelixChao/WizardDolphin-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FelixChao/WizardDolphin-7B

SGLang

How to use FelixChao/WizardDolphin-7B 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 "FelixChao/WizardDolphin-7B" \
    --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": "FelixChao/WizardDolphin-7B",
		"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 "FelixChao/WizardDolphin-7B" \
        --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": "FelixChao/WizardDolphin-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FelixChao/WizardDolphin-7B with Docker Model Runner:
```
docker model run hf.co/FelixChao/WizardDolphin-7B
```

Librarian Bot: Add base_model metadata to model

by librarian-bot - opened Jan 22, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

librarian-bot

Jan 22, 2024

This pull request aims to enrich the metadata of your model by adding base_model metadata to YAML block of your model's README.md.

How did we find this information? We use the mergekit_config.yml config from mergekit.

Why add this? Enhancing your model's metadata in this way:

Boosts Discoverability - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
Highlights Impact - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at librarian-bots/base_model_explorer.

This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien.

Librarian Bot: Add base_model metadata to model22c8b2d1

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