Instructions to use Sweaterdog/Andy-3.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sweaterdog/Andy-3.6 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sweaterdog/Andy-3.6",
	filename="Andy-3.6.F16.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 Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sweaterdog/Andy-3.6:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sweaterdog/Andy-3.6:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sweaterdog/Andy-3.6: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 Sweaterdog/Andy-3.6:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sweaterdog/Andy-3.6: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 Sweaterdog/Andy-3.6:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sweaterdog/Andy-3.6:Q4_K_M

Use Docker

docker model run hf.co/Sweaterdog/Andy-3.6:Q4_K_M

LM Studio
Jan
Ollama
How to use Sweaterdog/Andy-3.6 with Ollama:
```
ollama run hf.co/Sweaterdog/Andy-3.6:Q4_K_M
```

Unsloth Studio

How to use Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sweaterdog/Andy-3.6 to start chatting

Atomic Chat new
Docker Model Runner
How to use Sweaterdog/Andy-3.6 with Docker Model Runner:
```
docker model run hf.co/Sweaterdog/Andy-3.6:Q4_K_M
```

Lemonade

How to use Sweaterdog/Andy-3.6 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sweaterdog/Andy-3.6:Q4_K_M

Run and chat with the model

lemonade run user.Andy-3.6-Q4_K_M

List all available models

lemonade list

Andy-3.6 / unsloth.F16.gguf

Sweaterdog

(Trained with Unsloth)

f9f1fbf verified over 1 year ago

Download

History Blame

15.2 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 29f1a87d66701a9281b24271e61c991f523c910eea08e6f033896361ea13717a
Size of remote file:: 15.2 GB
SHA256:: b5202182de13d9a050fab434dd89ab9e608ad1a8c9562dcdd168ee79069a6cc7

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.