Instructions to use QuantFactory/G2-9B-Aletheia-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/G2-9B-Aletheia-v1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/G2-9B-Aletheia-v1-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/G2-9B-Aletheia-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/G2-9B-Aletheia-v1-GGUF",
	filename="G2-9B-Aletheia-v1.Q2_K.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/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/G2-9B-Aletheia-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/G2-9B-Aletheia-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/G2-9B-Aletheia-v1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/G2-9B-Aletheia-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-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/G2-9B-Aletheia-v1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/G2-9B-Aletheia-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/G2-9B-Aletheia-v1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/G2-9B-Aletheia-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/G2-9B-Aletheia-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.G2-9B-Aletheia-v1-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/G2-9B-Aletheia-v1-GGUF

This is quantized version of allura-org/G2-9B-Aletheia-v1 created using llama.cpp

Original Model Card

_{Image by CalamitousFelicitouness}

Gemma-2-9B Aletheia v1

A merge of Sugarquill and Sunfall. I wanted to combine Sugarquill's more novel-like writing style with something that would improve it's RP perfomance and make it more steerable, w/o adding superfluous synthetic writing patterns.

I quite like Crestfall's Sunfall models and I felt like Gemma version of Sunfall will steer the model in this direction when merged in. To keep more of Gemma-2-9B-it-SPPO-iter3's smarts, I've decided to apply Sunfall LoRA on top of it, instead of using the published Sunfall model.

I'm generally pleased with the result, this model has nice, fresh writing style, good charcard adherence and good system prompt following. It still should work well for raw completion storywriting, as it's a trained feature in both merged models.

Made by Auri.

Thanks to Prodeus, Inflatebot and ShotMisser for testing and giving feedback.

Format

Model responds to Gemma instruct formatting, exactly like it's base model.

<bos><start_of_turn>user
{user message}<end_of_turn>
<start_of_turn>model
{response}<end_of_turn><eos>

Mergekit config

The following YAML configuration was used to produce this model:

models:
  - model: allura-org/G2-9B-Sugarquill-v0
    parameters:
      weight: 0.55
      density: 0.4
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3+AuriAetherwiing/sunfall-g2-lora
    parameters:
      weight: 0.45
      density: 0.3
merge_method: ties
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
parameters:
  normalize: true
dtype: bfloat16

Downloads last month: 12

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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

Model tree for QuantFactory/G2-9B-Aletheia-v1-GGUF

UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3

allura-org/G2-9B-Sugarquill-v0

crestf411/gemma2-9B-sunfall-v0.5.2

Merge model

this model