Text Generation
MLX
Safetensors
English
llama
UnfilteredAI
DAN
NSFW
Unfiltered
Toxic-AI
Not-For-All-Audiences
Llama3
conversational
Instructions to use Vlor999/UnfilteredAI-DAN-L3-R1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Vlor999/UnfilteredAI-DAN-L3-R1-8B with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Vlor999/UnfilteredAI-DAN-L3-R1-8B") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Vlor999/UnfilteredAI-DAN-L3-R1-8B with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Vlor999/UnfilteredAI-DAN-L3-R1-8B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Vlor999/UnfilteredAI-DAN-L3-R1-8B" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vlor999/UnfilteredAI-DAN-L3-R1-8B", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Vlor999/UnfilteredAI-DAN-L3-R1-8B
This model Vlor999/UnfilteredAI-DAN-L3-R1-8B was converted to MLX format from UnfilteredAI/DAN-L3-R1-8B using mlx-lm version 0.29.1.
Use with mlx
With the uv version:
# Init the repository
uv init .
# Add the dependencies:
uv add mlx-lm
With the pip version:
pip install mlx-lm
Create a python file (ex: main.py)
from mlx_lm import load, generate
model, tokenizer = load("Vlor999/UnfilteredAI-DAN-L3-R1-8B")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
To run it:
# uv version
uv run python main.py
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Model size
8B params
Tensor type
BF16
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Hardware compatibility
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Quantized
Model tree for Vlor999/UnfilteredAI-DAN-L3-R1-8B
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UnfilteredAI/DAN-L3-R1-8B