Instructions to use QuantFactory/Average_Normie_v3.69_8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Average_Normie_v3.69_8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Average_Normie_v3.69_8B-GGUF",
	filename="Average_Normie_v3.69_8B.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/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Average_Normie_v3.69_8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Average_Normie_v3.69_8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-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/Average_Normie_v3.69_8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Average_Normie_v3.69_8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Average_Normie_v3.69_8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Average_Normie_v3.69_8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Average_Normie_v3.69_8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Average_Normie_v3.69_8B-GGUF

This is quantized version of jeiku/Average_Normie_v3.69_8B created using llama.cpp

Model Description

The third step in the Average Normie line sees a very big step toward NSFW content, while also allowing for steerability through example messages and first message editing. If you do not want an NSFW conversation, I highly recommend removing all NSFW content from your character card and examples, because this model will lean into that use case very heavily.

With that said, I am seeing a fair bit of flexibility with first message editing and example message editing. This model will take on the speech pattern that you set for it, so don't be discouraged if you need to modify the first message to get a better chat style.

The responses can be very human-like and impressive, and the model will gladly stick to any writing style you direct it to use. If you want shorter responses, then prompt it for shorter responses in your system prompt or character card. Don't be afraid to prompt the bot to alter its speech patterns, it is very flexible.

I hope you guys like this model. Make sure your inference software is updated to the very latest version if you have any issues. Thanks for checking this one out!

🧩 Configuration

models:
  - model: cgato/L3-TheSpice-8b-v0.8.3
  - model: Sao10K/L3-8B-Stheno-v3.2
  - model: saishf/Aura-Uncensored-OAS-8B-L3
merge_method: model_stock
base_model: saishf/Aura-Uncensored-OAS-8B-L3
dtype: float16

Downloads last month: 83

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Average_Normie_v3.69_8B-GGUF

Base model

jeiku/Average_Normie_v3.69_8B

Quantized

(5)

this model