Instructions to use Quant-Cartel/Luca-MN-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quant-Cartel/Luca-MN-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quant-Cartel/Luca-MN-iMat-GGUF",
	filename="Luca-MN-iMat-IQ1_M.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 Quant-Cartel/Luca-MN-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/Luca-MN-iMat-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 Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/Luca-MN-iMat-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 Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Quant-Cartel/Luca-MN-iMat-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 Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Quant-Cartel/Luca-MN-iMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quant-Cartel/Luca-MN-iMat-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": "Quant-Cartel/Luca-MN-iMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M

Ollama
How to use Quant-Cartel/Luca-MN-iMat-GGUF with Ollama:
```
ollama run hf.co/Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Quant-Cartel/Luca-MN-iMat-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 Quant-Cartel/Luca-MN-iMat-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 Quant-Cartel/Luca-MN-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Quant-Cartel/Luca-MN-iMat-GGUF to start chatting

Docker Model Runner
How to use Quant-Cartel/Luca-MN-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M
```

Lemonade

How to use Quant-Cartel/Luca-MN-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quant-Cartel/Luca-MN-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Luca-MN-iMat-GGUF-Q4_K_M

List all available models

lemonade list

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PROUDLY PRESENTS

Luca-MN-iMat-GGUF

Quantized with love from fp32.

Importance Matrix calculated using groups_merged.txt
92 chunks
n_ctx=512
Importance Matrix uses fp32 precision model weights, fp32.imatrix file to be added in repo

Original model README here and below:

Luca-MN-iMat-GGUF

This thing was just intended as an experiment but it turned out quite good. I had it both name and prompt imagegen for itself.

Created by running a high-r LoRA-pass over Nemo-Base with 2 epochs of some RP data, then a low-r pass with 0.5 epochs of the c2-data, then 3 epochs of DPO using jondurbin/gutenberg-dpo-v0.1.

Prompting

Use the Mistral V3-Tekken context- and instruct-templates. Temperature at about 1.25 seems to be the sweet spot, with either MinP at 0.05 or TopP at 0.9. DRY/Smoothing etc depending on your preference.

Downloads last month: 109

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Quant-Cartel/Luca-MN-iMat-GGUF

Base model

unsloth/Mistral-Nemo-Base-2407-bnb-4bit

Finetuned

rAIfle/Luca-MN-bf16

Quantized

(2)

this model