Instructions to use LTC-AI-Labs/L2-7b-Zar-WVG-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LTC-AI-Labs/L2-7b-Zar-WVG-Test with Transformers:

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

pipe = pipeline("text-generation", model="LTC-AI-Labs/L2-7b-Zar-WVG-Test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LTC-AI-Labs/L2-7b-Zar-WVG-Test")
model = AutoModelForCausalLM.from_pretrained("LTC-AI-Labs/L2-7b-Zar-WVG-Test")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LTC-AI-Labs/L2-7b-Zar-WVG-Test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LTC-AI-Labs/L2-7b-Zar-WVG-Test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LTC-AI-Labs/L2-7b-Zar-WVG-Test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LTC-AI-Labs/L2-7b-Zar-WVG-Test

SGLang

How to use LTC-AI-Labs/L2-7b-Zar-WVG-Test 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 "LTC-AI-Labs/L2-7b-Zar-WVG-Test" \
    --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": "LTC-AI-Labs/L2-7b-Zar-WVG-Test",
		"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 "LTC-AI-Labs/L2-7b-Zar-WVG-Test" \
        --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": "LTC-AI-Labs/L2-7b-Zar-WVG-Test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LTC-AI-Labs/L2-7b-Zar-WVG-Test with Docker Model Runner:
```
docker model run hf.co/LTC-AI-Labs/L2-7b-Zar-WVG-Test
```

Zarafusion trained on the WVG uncensored dataset

1025 steps

Honestly it's quite decent, according to my test on LavernAI. But I'm figuring out how to solve the spacing issues lol

Downloads last month: 3

LTC-AI-Labs
/

L2-7b-Zar-WVG-Test

Dataset used to train LTC-AI-Labs/L2-7b-Zar-WVG-Test