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, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

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 / README.md

LunaticPython161

Upload folder using huggingface_hub

d1afb24 verified over 2 years ago

preview code

raw

history blame contribute delete

1.68 kB

	---
	tags:
	- merge
	- mergekit
	- lazymergekit
	- WizardLM/WizardMath-7B-V1.1
	- segolilylabs/Lily-Cybersecurity-7B-v0.2
	base_model:
	- WizardLM/WizardMath-7B-V1.1
	- segolilylabs/Lily-Cybersecurity-7B-v0.2
	---

	# CyberWitch-7B

	CyberWitch-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
	* [segolilylabs/Lily-Cybersecurity-7B-v0.2](https://huggingface.co/segolilylabs/Lily-Cybersecurity-7B-v0.2)

	## 🧩 Configuration

	```yaml
	slices:
	- sources:
	- model: WizardLM/WizardMath-7B-V1.1
	layer_range: [0, 32]
	- model: segolilylabs/Lily-Cybersecurity-7B-v0.2
	layer_range: [0, 32]
	merge_method: slerp
	base_model: WizardLM/WizardMath-7B-V1.1
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.5, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.5, 0.7, 0.3, 0]
	- value: 0.5
	dtype: bfloat16
	```

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "LunaticPython161/CyberWitch-7B"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```