Instructions to use QuantFactory/StarDust-12b-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/StarDust-12b-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/StarDust-12b-v2-GGUF",
	filename="StarDust-12b-v2.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/StarDust-12b-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/StarDust-12b-v2-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/StarDust-12b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/StarDust-12b-v2-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/StarDust-12b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/StarDust-12b-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/StarDust-12b-v2-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/StarDust-12b-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M

Ollama
How to use QuantFactory/StarDust-12b-v2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/StarDust-12b-v2-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/StarDust-12b-v2-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/StarDust-12b-v2-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/StarDust-12b-v2-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/StarDust-12b-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/StarDust-12b-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/StarDust-12b-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.StarDust-12b-v2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/StarDust-12b-v2-GGUF

This is quantized version of Luni/StarDust-12b-v2 created using llama.cpp

Original Model Card

StarDust-12b-v2

Quants

GGUF: mradermacher/StarDust-12b-v2-GGUF
weighted/imatrix GGUF: mradermacher/StarDust-12b-v2-i1-GGUF
exl2: lucyknada/Luni_StarDust-12b-v2-exl2

Description | Usecase

The result of this merge is in my opinion a more vibrant and less generic sonnet inspired prose, it's able to be gentle and harsh where asked.
The v2 uses the non-kto magnum which tends to have less "claudeism" (making the story feel rather repetitive)
Note on Non-Kto: There is a very big gap between people preferring and disliking the KTO. To make things easier, you can still use Luni/StarDust-12b-v1 which has the KTO version.
In early testing users have reported a much better experience in longer roleplays and a abillity to add a creative touch to the stable experiencve.

Just like with v1:

This model is intended to be used as a Role-playing model.
Its direct conversational output is... I can't even say it's luck, it's just not made for it.
Extension to Conversational output: The Model is designed for roleplay, direct instructing or general purpose is NOT recommended.

Prompting

Edit: ChatML has proven to be the BEST choice.

Both Mistral and ChatML should work though I had better results with ChatML: ChatML Example:

"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using Sao10K/MN-12B-Lyra-v3 as a base.

Models Merged

The following models were included in the merge:

Special Thanks

Special thanks to the SillyTilly and myself for helping me find the energy to finish this.

Downloads last month: 110

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/StarDust-12b-v2-GGUF

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

Gryphe/Pantheon-RP-1.6-12b-Nemo

Quantized

(13)

this model

Papers for QuantFactory/StarDust-12b-v2-GGUF

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 33

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19