Instructions to use perfectlygray/GoldFusionReiV3-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("perfectlygray/GoldFusionReiV3-12B-GGUF", dtype="auto")

llama-cpp-python

How to use perfectlygray/GoldFusionReiV3-12B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="perfectlygray/GoldFusionReiV3-12B-GGUF",
	filename="goldfusionreiv3-12b-q5_k_m.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 perfectlygray/GoldFusionReiV3-12B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_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 perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_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 perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Use Docker

docker model run hf.co/perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Ollama:
```
ollama run hf.co/perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
```

Unsloth Studio

How to use perfectlygray/GoldFusionReiV3-12B-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 perfectlygray/GoldFusionReiV3-12B-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 perfectlygray/GoldFusionReiV3-12B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for perfectlygray/GoldFusionReiV3-12B-GGUF to start chatting

How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M
```

Lemonade

How to use perfectlygray/GoldFusionReiV3-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull perfectlygray/GoldFusionReiV3-12B-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.GoldFusionReiV3-12B-GGUF-Q5_K_M

List all available models

lemonade list

Produced by Antigma Labs, Antigma Quantize Space

Follow Antigma Labs in X https://x.com/antigma_labs

Antigma's GitHub Homepage https://github.com/AntigmaLabs

Quantization Format (GGUF)

We use llama.cpp release b5572 for quantization. Original model: https://huggingface.co/pot99rta/GoldFusionReiV3-12B

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split
goldfusionreiv3-12b-q5_k_m.gguf	Q5_K_M	8.13 GB	False

Original Model Card

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using Delta-Vector/Rei-V3-KTO-12B as a base.

Models Merged

The following models were included in the merge:

pot99rta/GoldFusion-12B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Delta-Vector/Rei-V3-KTO-12B
    #no parameters necessary for base model
  - model: Delta-Vector/Rei-V3-KTO-12B
    parameters:
      density: 0.5
      weight: 0.5
  - model: pot99rta/GoldFusion-12B
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Delta-Vector/Rei-V3-KTO-12B
parameters:
  normalize: false
  int8_mask: true
dtype: float16

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download https://huggingface.co/perfectlygray/GoldFusionReiV3-12B-GGUF --include "goldfusionreiv3-12b-q5_k_m.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download https://huggingface.co/perfectlygray/GoldFusionReiV3-12B-GGUF --include "goldfusionreiv3-12b-q5_k_m.gguf/*" --local-dir ./

You can either specify a new local-dir (e.g. deepseek-ai_DeepSeek-V3-0324-Q8_0) or it will be in default hugging face cache

Downloads last month: 4

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

5-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for perfectlygray/GoldFusionReiV3-12B-GGUF

Base model

pot99rta/GoldFusionReiV3-12B

Quantized

(5)

this model

Paper for perfectlygray/GoldFusionReiV3-12B-GGUF

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19