Phi 2 Super GGUF

🚀 MaziyarPanahi/phi-2-super-GGUF

This repository contains GGUF format model files for abacaj/phi-2-super, facilitating text generation tasks.

🚀 Quick Start

Model Information

• Model creator: abacaj
• Original model: abacaj/phi-2-super

✨ Features

• Quantized Formats: Available in 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit quantizations.
• Multiple Libraries Support: Compatible with various clients and libraries like llama.cpp, text-generation-webui, etc.

📦 Installation

About GGUF

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

Here is a list of clients and libraries known to support GGUF:

• llama.cpp: The source project for GGUF, offering a CLI and a server option.
• text-generation-webui: A widely used web UI with many features and powerful extensions, supporting GPU acceleration.
• KoboldCpp: A fully - featured web UI with GPU accel across all platforms and GPU architectures, great 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 is 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](https://github.com/abetlen/llama - cpp - python): A Python library with GPU accel, LangChain support, and OpenAI - compatible API server.
• candle: A Rust ML framework focusing on performance, including GPU support and ease of use.
• ctransformers: A Python library with GPU accel, LangChain support, and OpenAI - compatible AI server. Note that as of November 27th, 2023, ctransformers has not been updated for a long time and does not support many recent models.

Explanation of quantisation methods

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

• LM Studio
• LoLLMS Web UI
• Faraday.dev

In text - generation - webui

Under Download Model, you can enter the model repo: [MaziyarPanahi/phi - 2 - super - GGUF](https://huggingface.co/MaziyarPanahi/phi - 2 - super - GGUF) and below it, a specific filename to download, such as: phi - 2 - super - GGUF.Q4_K_M.gguf. Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface - hub Python library:

pip3 install huggingface - hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface - cli download MaziyarPanahi/phi - 2 - super - GGUF phi - 2 - super - GGUF.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

huggingface - cli download [MaziyarPanahi/phi - 2 - super - GGUF](https://huggingface.co/MaziyarPanahi/phi - 2 - super - GGUF) --local - dir . --local - dir - use - symlinks False --include='*Q4_K*gguf'

pip3 install hf_transfer

HF_HUB_ENABLE_HF_TRANSFER = 1 huggingface - cli download MaziyarPanahi/phi - 2 - super - GGUF phi - 2 - super - GGUF.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

💻 Usage Examples

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m phi - 2 - super - GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change -c 32768 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat - style conversation, replace the -p <PROMPT> argument with -i -ins For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text - generation - webui

Further instructions can be found in the text - generation - webui documentation, here: [text - generation - webui/docs/04 ‚Äê Model Tab.md](https://github.com/oobabooga/text - generation - webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

How to run from Python code

You can use GGUF models from Python using the [llama - cpp - python](https://github.com/abetlen/llama - cpp - python) or ctransformers libraries. Note that at the time of writing (Nov 27th, 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore, I recommend you use llama - cpp - python.

How to load this model in Python code, using llama - cpp - python

For full documentation, please see: [llama - cpp - python docs](https://abetlen.github.io/llama - cpp - python/).

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama - cpp - python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama - cpp - python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama - cpp - python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama - cpp - python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama - cpp - python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama - cpp - python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama - cpp - python

Simple llama - cpp - python example code

from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./phi - 2 - super - GGUF.Q4_K_M.gguf",  # Download the model file first
  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./phi - 2 - super - GGUF.Q4_K_M.gguf", chat_format="llama - 2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama - cpp - python and ctransformers with LangChain:

• LangChain + llama - cpp - python
• LangChain + ctransformers

📄 License

This project is licensed under the MIT license.