Instructions to use LunaticPython161/CyberWitch-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LunaticPython161/CyberWitch-7B with Transformers:

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

pipe = pipeline("text-generation", model="LunaticPython161/CyberWitch-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LunaticPython161/CyberWitch-7B")
model = AutoModelForMultimodalLM.from_pretrained("LunaticPython161/CyberWitch-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LunaticPython161/CyberWitch-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LunaticPython161/CyberWitch-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LunaticPython161/CyberWitch-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LunaticPython161/CyberWitch-7B

SGLang

How to use LunaticPython161/CyberWitch-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 "LunaticPython161/CyberWitch-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": "LunaticPython161/CyberWitch-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 "LunaticPython161/CyberWitch-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": "LunaticPython161/CyberWitch-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LunaticPython161/CyberWitch-7B with Docker Model Runner:
```
docker model run hf.co/LunaticPython161/CyberWitch-7B
```

CyberWitch-7B / model-00001-of-00002.safetensors

LunaticPython161

Upload folder using huggingface_hub

d1afb24 verified over 2 years ago

Download

History Blame Contribute Delete

9.94 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 195c6f37532617a5239383ecd6ffecf1d1dc52b4b41162cd7c3c7b7cd7d39e00
Size of remote file:: 9.94 GB
SHA256:: d007ce2d37b8aa4020228bac88dcb07142702a08e2ec57278342fa6d985d4c8a

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.