Instructions to use concedo/Vicuzard-30B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use concedo/Vicuzard-30B-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="concedo/Vicuzard-30B-Uncensored", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("concedo/Vicuzard-30B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("concedo/Vicuzard-30B-Uncensored", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use concedo/Vicuzard-30B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "concedo/Vicuzard-30B-Uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "concedo/Vicuzard-30B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/concedo/Vicuzard-30B-Uncensored

SGLang

How to use concedo/Vicuzard-30B-Uncensored 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 "concedo/Vicuzard-30B-Uncensored" \
    --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": "concedo/Vicuzard-30B-Uncensored",
		"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 "concedo/Vicuzard-30B-Uncensored" \
        --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": "concedo/Vicuzard-30B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use concedo/Vicuzard-30B-Uncensored with Docker Model Runner:
```
docker model run hf.co/concedo/Vicuzard-30B-Uncensored
```

Vicuzard-30B-Uncensored

This is an experimental mixed model containing a parameter-wise 50/50 blend (weighted average) of ehartford/Wizard-Vicuna-30B-Uncensored and ehartford/WizardLM-30B-Uncensored

GGML models are provided here, for use in KoboldCPP.

This improves on earlier model mixing techniques by only applying the merge to the layers containing tensors of the same dimensions. By selectively skipping merge operations on the input and output layers, we are now able to merge models with different vocab sizes (i.e. added tokens) so long as the hidden layers have identical sizes.

All feedback and comments can be directed to Concedo on the KoboldAI discord.

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