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

Libraries

How to use QuantFactory/MN-12B-Starcannon-v2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/MN-12B-Starcannon-v2-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/MN-12B-Starcannon-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/MN-12B-Starcannon-v2-GGUF",
	filename="MN-12B-Starcannon-v2.Q4_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/MN-12B-Starcannon-v2-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/MN-12B-Starcannon-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/MN-12B-Starcannon-v2-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/MN-12B-Starcannon-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/MN-12B-Starcannon-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/MN-12B-Starcannon-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MN-12B-Starcannon-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/MN-12B-Starcannon-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MN-12B-Starcannon-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MN-12B-Starcannon-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/MN-12B-Starcannon-v2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MN-12B-Starcannon-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/MN-12B-Starcannon-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/MN-12B-Starcannon-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/MN-12B-Starcannon-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/MN-12B-Starcannon-v2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/MN-12B-Starcannon-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MN-12B-Starcannon-v2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MN-12B-Starcannon-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MN-12B-Starcannon-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MN-12B-Starcannon-v2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/MN-12B-Starcannon-v2-GGUF

This is quantized version of aetherwiing/MN-12B-Starcannon-v2 created using llama.cpp

Original Model Card

MN-12B-Starcannon-v2

A star and a gun is all you need

This is a merge of pre-trained language models created using mergekit. Turned out to be a bit more Magnum-esque, but still is very creative, and writing style is pretty nice, even if some slop words appear time to time. Might be a good fit for people wanting more variety than Magnum has, and more verbose prose than Celeste v1.9 has.

Dynamic FP8
Static GGUF (by Mradermacher)
EXL2 (by kingbri of RoyalLab)

Merge Details

Merge Method

This model was merged using the TIES merge method using nothingiisreal/MN-12B-Celeste-V1.9 as a base.

Merge fodder

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
    - model: intervitens/mini-magnum-12b-v1.1
      parameters:
        density: 0.3
        weight: 0.5
    - model: nothingiisreal/MN-12B-Celeste-V1.9
      parameters:
        density: 0.7
        weight: 0.5

merge_method: ties
base_model: nothingiisreal/MN-12B-Celeste-V1.9
parameters:
    normalize: true
    int8_mask: true
dtype: bfloat16

Downloads last month: 16

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

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Model tree for QuantFactory/MN-12B-Starcannon-v2-GGUF

intervitens/mini-magnum-12b-v1.1

nothingiisreal/MN-12B-Celeste-V1.9

Merge model

this model

Paper for QuantFactory/MN-12B-Starcannon-v2-GGUF

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19