Instructions to use QuantFactory/Azure_Dusk-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Azure_Dusk-v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Azure_Dusk-v0.1-GGUF",
	filename="Azure_Dusk-v0.1.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Azure_Dusk-v0.1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Azure_Dusk-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Azure_Dusk-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Azure_Dusk-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Azure_Dusk-v0.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Azure_Dusk-v0.1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Azure_Dusk-v0.1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Azure_Dusk-v0.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Azure_Dusk-v0.1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Azure_Dusk-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Azure_Dusk-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Azure_Dusk-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Azure_Dusk-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Azure_Dusk-v0.1-GGUF

This is quantized version of Epiculous/Azure_Dusk-v0.1 created using llama.cpp

Original Model Card

Flipping the training process that created Crimson Dawn on it's head, I present to you, Azure Dusk! While both models are built using Mistral-Nemo-Base-2407; Azure Dusk's training methodology was instruct first, then RP dataset applied after, however, the end goal reamains the same AI should not be a boring bland generic assistant, but something that you can connect with on a more personal level. Something that can be interesting in a Roleplay, but useful as an assistant too.

Quants!

full / exl2 / gguf

Prompting

Azure Dusk was trained with the Mistral Instruct template, therefore it should be prompted in a similar way that you would prompt any other mistral based model.

"<s>[INST] Prompt goes here [/INST]<\s>"

Context and Instruct

Magnum-123B-Context.json
Magnum-123B-Instruct.json
*** NOTE ***
There have been reports of the quantized model misbehaving with the mistral prompt, if you are seeing issues it may be worth trying ChatML Context and Instruct templates. If you are using GGUF I strongly advise using ChatML, for some reason that quantization performs better using ChatML.

Current Top Sampler Settings

Violet_Twilight-Nitral-Special- Considered the best settings!
Crimson_Dawn-Nitral-Special
Crimson_Dawn-Magnum-Style

Tokenizer

If you are using SillyTavern, please set the tokenizer to API (WebUI/ koboldcpp)

Training

Training was done twice over 2 epochs each on two 2x NVIDIA A6000 GPUs using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on Instruct data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on RP, and the new RP LoRA was applied to the modified base, resulting in what you see here.

Special Thanks

Special thanks to my friends over at Anthracite! Without their help and Kalomaze starting the synthetic data script, none of this would have been possible. Also want to thank my friends in The Chaotic Neutrals for their friendship, support, and guidance.

Downloads last month: 9

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit