This repo contains GGUF format model files for OpenBuddy's OpenBuddy Llama2 70B v13 Base.

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 incomplate list of clients and libraries that are known to support GGUF:

{prompt}

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.

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.

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 openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-* > openbuddy-llama2-70b-v13-base.Q6_K.gguf && rm openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-*
cat openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-* > openbuddy-llama2-70b-v13-base.Q8_0.gguf && rm openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-*

COPY /B openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-a + openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-b openbuddy-llama2-70b-v13-base.Q6_K.gguf
del openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-a openbuddy-llama2-70b-v13-base.Q6_K.gguf-split-b

COPY /B openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-a + openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-b openbuddy-llama2-70b-v13-base.Q8_0.gguf
del openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-a openbuddy-llama2-70b-v13-base.Q8_0.gguf-split-b

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:

Under Download Model, you can enter the model repo: TheBloke/openbuddy-llama2-70B-v13-base-GGUF and below it, a specific filename to download, such as: openbuddy-llama2-70b-v13-base.Q4_K_M.gguf.

Then click Download.

I recommend using the huggingface-hub Python library:

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

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

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

Change -c 4096 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.

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

Further instructions here: text-generation-webui/docs/llama.cpp.md.

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

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

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

For further support, and discussions on these models and AI in general, join us at:

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

This is a part of the Base-series models, trained utilizing approximately 50% of conversational data. It embodies cognitive and dialogue capabilities parallel to the fully-trained OpenBuddy models, yet it hasn’t been extensively fine-tuned for generic conversational tasks.

We released this model intending to empower the community, enabling further fine-tuning and deployment of specialized, domain-specific models.

For immediate use in generic conversations, consider referring to our versions that without the -base suffix.

GitHub and Usage Guide: https://github.com/OpenBuddy/OpenBuddy

Website and Demo: https://openbuddy.ai

Evaluation result of this model: Evaluation.txt

This model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.

This model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.

If you have not obtained approval from Meta, you must visit the https://ai.meta.com/llama/ page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.

All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.

OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.

By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.

所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。

OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。

使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。