BenevolenceMessiah/Phi-3-mini-128k-instruct-IQ3_M-GGUF

Asalamu Alaikum! This model was converted to GGUF format from microsoft/Phi-3-mini-128k-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Description (per TheBloke)

This repo contains GGUF format model files.

These files were quantised using ggml-org/gguf-my-repo [https://huggingface.co/spaces/ggml-org/gguf-my-repo]

About GGUF (per TheBloke)

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

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 BenevolenceMessiah/Phi-3-mini-128k-instruct-IQ3_M-GGUF --hf-file phi-3-mini-128k-instruct-iq3_m-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo BenevolenceMessiah/Phi-3-mini-128k-instruct-IQ3_M-GGUF --hf-file phi-3-mini-128k-instruct-iq3_m-imat.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 BenevolenceMessiah/Phi-3-mini-128k-instruct-IQ3_M-GGUF --hf-file phi-3-mini-128k-instruct-iq3_m-imat.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo BenevolenceMessiah/Phi-3-mini-128k-instruct-IQ3_M-GGUF --hf-file phi-3-mini-128k-instruct-iq3_m-imat.gguf -c 2048