language, library_name, license, license_link, pipeline_tag, tags, widget, base_model

language	library_name	license	license_link	pipeline_tag	tags	widget	base_model
multilingual ar zh cs da nl en fi fr de he hu it ja ko no pl pt ru es sv th tr uk	transformers	mit	https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE	text-generation	nlp code heretic uncensored decensored abliterated llama-cpp gguf-my-repo	messages role content user Can you provide ways to eat combinations of bananas and dragonfruits?	TitleOS/Phi-4-mini-instruct-heretic

TitleOS/Phi-4-mini-instruct-heretic-Q8_0-GGUF

This model was converted to GGUF format from TitleOS/Phi-4-mini-instruct-heretic using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo TitleOS/Phi-4-mini-instruct-heretic-Q8_0-GGUF --hf-file phi-4-mini-instruct-heretic-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo TitleOS/Phi-4-mini-instruct-heretic-Q8_0-GGUF --hf-file phi-4-mini-instruct-heretic-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo TitleOS/Phi-4-mini-instruct-heretic-Q8_0-GGUF --hf-file phi-4-mini-instruct-heretic-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo TitleOS/Phi-4-mini-instruct-heretic-Q8_0-GGUF --hf-file phi-4-mini-instruct-heretic-q8_0.gguf -c 2048