Instructions to use EnlistedGhost/Tigerlily-R3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EnlistedGhost/Tigerlily-R3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EnlistedGhost/Tigerlily-R3-GGUF",
	filename="Tigerlily-R3-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use EnlistedGhost/Tigerlily-R3-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EnlistedGhost/Tigerlily-R3-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 EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EnlistedGhost/Tigerlily-R3-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 EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EnlistedGhost/Tigerlily-R3-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 EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use EnlistedGhost/Tigerlily-R3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EnlistedGhost/Tigerlily-R3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EnlistedGhost/Tigerlily-R3-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M

Ollama
How to use EnlistedGhost/Tigerlily-R3-GGUF with Ollama:
```
ollama run hf.co/EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use EnlistedGhost/Tigerlily-R3-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 EnlistedGhost/Tigerlily-R3-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 EnlistedGhost/Tigerlily-R3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EnlistedGhost/Tigerlily-R3-GGUF to start chatting

Docker Model Runner
How to use EnlistedGhost/Tigerlily-R3-GGUF with Docker Model Runner:
```
docker model run hf.co/EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M
```

Lemonade

How to use EnlistedGhost/Tigerlily-R3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EnlistedGhost/Tigerlily-R3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tigerlily-R3-GGUF-Q4_K_M

List all available models

lemonade list

Model Details and Specifications:

This release contains:
Llama.cpp and Ollama compatible GGUF converted and Quantized model files (Compatible with both Ollama, and Llama.cpp)
(More information and an updates to the ModelCard (this page) coming soon!)

Quantized GGUF version of:

jcathuriges/tigerlily-r3
(by inflatebot)

Original Model Link:

[cathuriges/tigerlily-r3]

GGUF Conversion and Quantization Details:

Software used to convert Safetensors to GGUF:

llama.cpp

Software used to create Quantized GGUF Files:

llama.cpp

Specific GitHub Commit Point:

b7099

Converted to GGUF and Quantized by:

EnlistedGhost

Original Info

(Crossposted from the link in the above section: "Model Details"):

tigerlily-r3

This is a merge of pre-trained language models created using mergekit.

- Merge Details -
Bumped Tiger up a skosh from (unpublished) R2. Mix seems good now. Project continues. Merge Method This model was merged using the Task Arithmetic merge method using grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated as a base. Models Merged The following models were included in the merge: