How to use from
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 noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
# Run inference directly in the terminal:
llama cli -hf noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
# Run inference directly in the terminal:
llama cli -hf noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
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 noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
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 noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
Use Docker
docker model run hf.co/noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF:
Quick Links

These are quantizations of the model LiquidAI / LFM2.5-2.6B.

Abliterated with heretic 1.4.0

The quantizations were created using an imatrix merged from combined_en_medium and harmful.txt to leverage the abliterated nature of the model.

Metric This model Original model
Refusals 7/100 96/100
KL divergence 0.0146 0
Downloads last month
4,982
GGUF
Model size
3B params
Architecture
lfm2
Hardware compatibility
Log In to add your hardware

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for noctrex/LFM2.5-2.6B-heretic-uncensored-GGUF

Quantized
(82)
this model