Instructions to use QuantFactory/Hathor-L3-8B-v.02-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Hathor-L3-8B-v.02-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Hathor-L3-8B-v.02-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-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/Hathor-L3-8B-v.02-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Hathor-L3-8B-v.02-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Hathor-L3-8B-v.02-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Hathor-L3-8B-v.02-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hathor-L3-8B-v.02-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Hathor-L3-8B-v.02-GGUF

This is quantized version of Nitral-AI/Hathor-L3-8B-v.02 created using llama.cpp

Model Description

"Hathor-v0.2 is a model based on the LLaMA 3 architecture: Designed to seamlessly integrate the qualities of creativity, intelligence, and robust performance. Making it an ideal tool for a wide range of applications; such as creative writing, educational support and human/computer interaction."

Recomended ST Presets: Hathor Presets

Notes: Hathor is trained on 3 epochs of private data, synthetic opus instructons, a mix of light/classical novel data, roleplaying chat pairs over llama 3 8B instruct. (expanded)

If you want to use vision functionality:

You must use the latest versions of Koboldcpp.

To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo. Llava MMProj

You can load the mmproj by using the corresponding section in the interface:

Downloads last month: 304

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/Hathor-L3-8B-v.02-GGUF

Base model

Nitral-AI/Hathor_Stable-v0.2-L3-8B

Quantized

(12)

this model