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. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
Faraday.dev, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
localGPT An open-source initiative enabling private conversations with documents.

Explanation of quantisation methods

Click to see details

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.

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 folder.

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: LiteLLMs/Llama-3-MAAL-8B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.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 LiteLLMs/Llama-3-MAAL-8B-Instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download LiteLLMs/Llama-3-MAAL-8B-Instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install huggingface_hub[hf_transfer]

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama-3-MAAL-8B-Instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

## Example `llama.cpp` command

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

./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 8192 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 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the 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.

First install the package

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

shell

# 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

python

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="./Q4_0/Q4_0-00001-of-00009.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(
  "<PROMPT>", # 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="./Q4_0/Q4_0-00001-of-00009.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:

Original model card: Llama-3-MAAL-8B-Instruct-v0.1

Llama-3-MAAL-8B-Instruct-v0.1

we release MAAL, Multilingual Adaptive Augmentation Language-model which comprises a groundbreaking fusion of multilingual capabilities and adaptive augmentation techniques.

Developed by: maum.ai Brain NLP. Jaeyoon Jung, Jinjoo Lee, Yongjae Lee, Dongjun Lee, Woosung Joo
Language(s) (NLP): Korean, English (currently, bilingual)

Model Description

Version 0.1 uses cross-lingual training to transfer instruction-following capabilities from English to Korean.

We Trained this model on an 8 H100-80G for 1 day with cross-lingual training dataset
we recommend using the fixed system prompt for the model unless you fine-tune it

너는 마음에이아이의 챗봇 MAAL이다. 고객의 질문에 친절하게 답하여라.

sample inference code (GPU)

import transformers
import torch

model_id = "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1"
model = transformers.AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
streamer = transformers.TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# we recommend using the fixed prompt for the model unless you fine-tune it
prompt = "너는 마음에이아이의 챗봇 MAAL이다. 고객의 질문에 친절하게 답하여라."
instruction = "사과 한 박스에는 사과가 30개 들어있는데, 처음에는 사과 3박스가 있었고, 내가 사과 5개를 먹었어. 남은 사과는 총 몇개야?"

messages = [
    {"role": "system", "content": f"{prompt}"},
    {"role": "user", "content": f"{instruction}"}
    ]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors='pt').to("cuda")
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=1024, pad_token_id=tokenizer.eos_token_id)

Evaluation Results

As the main goal of version 0.1 is to transfer instruction-following capabilities from English to Korean without utilizing continuous pre-training, etc., we select LogicKor as our evaluation method to assess the Korean instruction skills.

We compare our model with a similar parameter model (less than 13B) that has been fine-tuned on the Korean dataset. * denotes our self-report result.

| Model | single-turn(↑) | multi-turn(↑) | average(↑) | | - | | maum-ai/Llama-3-MAAL-8B-Instruct-v0.1* | 5.80 | 4.66 | 5.23 | | maywell/Synatra-kiqu-10.7B | 5.71 | 4.73 | 5.22 | | yanolja/EEVE-Korean-Instruct-10.8B-v1.0 | 5.78 | 3.92 | 4.85 | | nlpai-lab/KULLM3 | 4.61 | 4.83 | 4.72 | | MLP-KTLim/llama3-Bllossom* | 2.11 | 1.57 | 1.84 |

Limitations

Due to this model being trained on a small dataset, it has several limitations.

Hard to generate diverse Korean texts
lack of Korean knowledge & Culture (localization)
Not work with Image inputs and video inputs

Todo

we will solve these limitations one by one by upgrading this model like as...

Enhance the Korean generation through Vocabulary Expansion & Continuous pre-training. (more Korean corpus!)
Localize with cultural adaptation method and additional Korean knowledge data. similar idea
Develop a Vision Language Model that can handle both video and image inputs. similar idea

LiteLLMs/Llama-3-MAAL-8B-Instruct-v0.1-GGUF

Llama-3-MAAL-8B-Instruct-v0.1-GGUF

Description

About GGUF