Instructions to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat",
	filename="Hathor_Stable-L3-8B-v0.5-IQ3_M-imat.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 ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat: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 ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat: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 ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M

Use Docker

docker model run hf.co/ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M

LM Studio
Jan
Ollama
How to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with Ollama:
```
ollama run hf.co/ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
```

Unsloth Studio

How to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat 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 ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat 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 ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat to start chatting

Atomic Chat new
Docker Model Runner
How to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with Docker Model Runner:
```
docker model run hf.co/ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M
```

Lemonade

How to use ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ABX-AI/Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat:Q4_K_M

Run and chat with the model

lemonade run user.Hathor_Fractionate-L3-8B-v.05-GGUF-IQ-Imat-Q4_K_M

List all available models

lemonade list

GGUF / IQ / Imat for: https://huggingface.co/Nitral-AI/Hathor_Fractionate-L3-8B-v.05

ORIGINAL MODEL CARD BELOW:

"Hathor-v0.5 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(Updated)

Quants From Bartowski <3: https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-exl2 https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF

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. (update- More Private RP, Cybersecurity, Programming, Biology/Anatomy data.)

Downloads last month: 43

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

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