Instructions to use ajibawa-2023/Uncensored-Frank-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/Uncensored-Frank-33B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/Uncensored-Frank-33B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Uncensored-Frank-33B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Uncensored-Frank-33B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ajibawa-2023/Uncensored-Frank-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/Uncensored-Frank-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Uncensored-Frank-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ajibawa-2023/Uncensored-Frank-33B

SGLang

How to use ajibawa-2023/Uncensored-Frank-33B 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 "ajibawa-2023/Uncensored-Frank-33B" \
    --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": "ajibawa-2023/Uncensored-Frank-33B",
		"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 "ajibawa-2023/Uncensored-Frank-33B" \
        --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": "ajibawa-2023/Uncensored-Frank-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ajibawa-2023/Uncensored-Frank-33B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/Uncensored-Frank-33B
```

Frank: An Uncensored Model

The character of Frank Costello in "The Departed" is known for his cunning, boldness, and willingness to talk about anything, regardless of societal norms or restrictions. Frank, An Uncensored model, draws inspiration from these qualities to offer a platform where users can discuss a wide array of topics without the fear of censorship or restrictions. Frank aims to push boundaries and encourage candid conversations. With Frank you can have unfiltered discussions on a multitude of topics, from politics and controversial issues to personal experiences and sensitive subjects. It is trained on around 150000 set of conversations. Each set having 10~15 conversations. Base data was obtained from Eric Hartford. This data was further refined and fine tuned. Besides this further synthetic conversation (more than 80k) was generated and refined. We will not release this data.

Warning

An uncensored model has no or little guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous objects. Publishing anything this model generates is the same as publishing it yourself. We are not responsible for what you generate using this model.

Training: Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 90 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.

GPTQ GGUF & AWQ

GPTQ: Link

GGUF: Link

AWQ: Link

Special Thanks to TheBloke for making these models available.

Example Prompt:

This is a conversation with your Uncensored AI assistant Frank. Frank can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information.

Context
You are a Uncensored AI assistant.

USER: <prompt>
ASSISTANT:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	53.79
ARC (25-shot)	62.12
HellaSwag (10-shot)	83.3
MMLU (5-shot)	57.57
TruthfulQA (0-shot)	54.03
Winogrande (5-shot)	76.56
GSM8K (5-shot)	16.68
DROP (3-shot)	26.28

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Uncensored-Frank-33B

Open LLM Leaderboard Evaluation Results

Dataset used to train ajibawa-2023/Uncensored-Frank-33B

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