Swallow 70B Instruct GGUF

🚀 Swallow 70B Instruct - GGUF

This repository provides GGUF format model files for the Swallow 70B Instruct model. It offers various quantized versions suitable for different hardware and use - cases, enabling efficient inference on both CPU and GPU.

🚀 Quick Start

Prerequisites

• Install necessary libraries for downloading models, such as huggingface - hub.

pip3 install huggingface - hub

Download a Model File

You can download a specific model file, like swallow - 70b - instruct.Q4_K_M.gguf, to the current directory using the following command:

huggingface - cli download TheBloke/Swallow - 70B - instruct - GGUF swallow - 70b - instruct.Q4_K_M.gguf --local - dir. --local - dir - use - symlinks False

✨ Features

• Multiple Quantization Methods: Offers a wide range of quantization methods (Q2_K, Q3_K, Q4_K, etc.) to balance between model size and quality.
• Broad Compatibility: Compatible with many popular clients and libraries, including llama.cpp, text - generation - webui, KoboldCpp, etc.
• Multilingual Support: Supports both English and Japanese languages.

📦 Installation

Install Required Libraries

If you want to use the model in Python, you can install llama - cpp - python with different acceleration options:

# 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

💻 Usage Examples

Basic Usage in llama.cpp

./main -ngl 35 -m swallow - 70b - instruct.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "以下に、あるタスクを説明する指示があります。リクエストを適切に完了するための回答を記述してください。\n\n### 指示:\n{prompt}\n\n### 応答:"

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="./swallow - 70b - instruct.Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # 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(
  "以下に、あるタスクを説明する指示があります。リクエストを適切に完了するための回答を記述してください。\n\n### 指示:\n{prompt}\n\n### 応答:", # 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="./swallow - 70b - instruct.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."
        }
    ]
)

📚 Documentation

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 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](https://github.com/oobabooga/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](https://github.com/ParisNeo/lollms - webui), 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 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.

Repositories available

• [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Swallow - 70B - instruct - AWQ)
• [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Swallow - 70B - instruct - GPTQ)
• [2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Swallow - 70B - instruct - GGUF)
• [tokyotech - llm's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/tokyotech - llm/Swallow - 70b - instruct - hf)

Prompt template: Swallow - Instruct

以下に、あるタスクを説明する指示があります。リクエストを適切に完了するための回答を記述してください。\n\n### 指示:\n{prompt}\n\n### 応答:

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third - party UIs and libraries - please see the list at the top of this README.

Explanation of quantisation methods

Provided files

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

Q6_K and Q8_0 files are split and require joining

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

cat swallow - 70b - instruct.Q6_K.gguf - split - * > swallow - 70b - instruct.Q6_K.gguf && rm swallow - 70b - instruct.Q6_K.gguf - split - *
cat swallow - 70b - instruct.Q8_0.gguf - split - * > swallow - 70b - instruct.Q8_0.gguf && rm swallow - 70b - instruct.Q8_0.gguf - split - *

COPY /B swallow - 70b - instruct.Q6_K.gguf - split - a + swallow - 70b - instruct.Q6_K.gguf - split - b swallow - 70b - instruct.Q6_K.gguf
del swallow - 70b - instruct.Q6_K.gguf - split - a swallow - 70b - instruct.Q6_K.gguf - split - b

COPY /B swallow - 70b - instruct.Q8_0.gguf - split - a + swallow - 70b - instruct.Q8_0.gguf - split - b swallow - 70b - instruct.Q8_0.gguf
del swallow - 70b - instruct.Q8_0.gguf - split - a swallow - 70b - instruct.Q8_0.gguf - split - b

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: TheBloke/Swallow - 70B - instruct - GGUF and below it, a specific filename to download, such as: swallow - 70b - instruct.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 TheBloke/Swallow - 70B - instruct - GGUF swallow - 70b - instruct.Q4_K_M.gguf --local - dir. --local - dir - use - symlinks False

huggingface - cli download TheBloke/Swallow - 70B - instruct - 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 TheBloke/Swallow - 70B - instruct - GGUF swallow - 70b - instruct.Q4_K_M.gguf --local - dir. --local - dir - use - symlinks False

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](https://github.com/oobabooga/text - generation - webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

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 model is under the llama2 license.

Discord

For further support, and discussions on these models and AI in general, join us at: TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from [gpus.llm - utils.org](llm - utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine - tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

• Patreon: https://patreon.com/TheBlokeAI
• Ko - Fi: https://ko - fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel - Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann - Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous support.