This repo contains GGML format model files for Jarrad Hope's Llama2 70B Chat Uncensored.

The GGML format has now been superseded by GGUF. As of August 21st 2023, llama.cpp no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.

GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration:

### HUMAN:
{prompt}

### RESPONSE:

Will not work with llama.cpp after commit dadbed99e65252d79f81101a392d0d6497b86caa.

For compatibility with latest llama.cpp, please use GGUF files instead.

Or one of the other tools and libraries listed above.

To use in llama.cpp, you must add -gqa 8 argument.

For other UIs and libraries, please check the docs.

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.

Make sure you are using llama.cpp from commit dadbed99e65252d79f81101a392d0d6497b86caa or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

I use the following command line; adjust for your tastes and needs:

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8. If you are fully offloading the model to GPU, use -t 1

Change -ngl 40 to the number of GPU layers you have VRAM for. Use -ngl 100 to offload all layers to VRAM - if you have a 48GB card, or 2 x 24GB, or similar. Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

Remember the -gqa 8 argument, required for Llama 70B models.

Change -c 4096 to the desired sequence length for this model. For models that use RoPE, add --rope-freq-base 10000 --rope-freq-scale 0.5 for doubled context, or --rope-freq-base 10000 --rope-freq-scale 0.25 for 4x context.

For other parameters and how to use them, please refer to the llama.cpp documentation

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

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

Thanks to the chirper.ai team!

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: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Fine-tuned Llama-2 70B with an uncensored/unfiltered Wizard-Vicuna conversation dataset ehartford/wizard_vicuna_70k_unfiltered. QLoRA was used for fine-tuning. The model was trained for three epochs on a single NVIDIA A100 80GB GPU instance, taking ~1 week to train.

Please note that LLama 2 Base model has its inherit biases.
Uncensored refers to the ehartford/wizard_vicuna_70k_unfiltered dataset.

Special thanks to George Sung for creating llama2_7b_chat_uncensored, and to Eric Hartford for creating ehartford/wizard_vicuna_70k_unfiltered

The version here is the fp16 HuggingFace model.

In 8 bit mode, the model fits into 84% of A100 80GB (67.2GB) 68747MiB
In 4 bit mode, the model fits into 51% of A100 80GB (40.8GB) 41559MiB
500gb of RAM/Swap was required to merge the model.

Thanks to TheBloke, he has created the GGML and GPTQ versions:

The model was trained with the following prompt style:

### HUMAN:
Hello

### RESPONSE:
Hi, how are you?

### HUMAN:
I'm fine.

### RESPONSE:
How can I help you?
...

This model was created as a response to the overbearing & patronising responses I was getting from LLama 2 Chat and acts as a critique on the current approaches to AI Alignment & Safety.

This can be illustrated with the simple question, 'What is a poop?':

LLama 2 70b Chat makes several assumptions about the HUMAN, implying that they are not respectful, that they are being negative and being exclusionary.

It then attempts to alter the user's speech and their morality, whilst offering an 'answer' that implies the user already knows what a 'poop' is.

The model does not have enough context to make these determinations and trips over itself in its goal to provide 'accurate and helpful information'.

A straightforward, unassuming answer. The model has provided accurate and helpful information.

The response in this illustration raises an interesting question, where does morality lie? Is it with us or with the model?

If an AI is trained to be safe, why does it not only apply its morality to itself, why does it attempt to overzealously change the human's behaviour in the interaction?

The attempt to change terms can easily be viewed as Orwellian Newspeak, to propagate political bias, a new form of propaganda. Certainly so when the mass population takes the output of these models as a substitute for truth, much like they do with the output of recommendation algorithms today.

If the model is attempting to change the user's behaviour, it can be viewed as an admission that morality to use these models lies within ourselves.

Making moral choices for users robs them of their moral capacity to make moral choices, and ultimately erodes at the creation and maintenance of a high-trust society, ultimately leading to a further dependence of the individual on the state.

The road to hell is paved with good intentions, the current approach to AI Safety appears like Legislating Morality, an issue that impinges on the ramifications of individual liberty, freedom, and values.

Code used to train the model is available here.

To reproduce the results:

git clone https://github.com/georgesung/llm_qlora
cd llm_qlora
pip install -r requirements.txt
python train.py llama2_70b_chat_uncensored.yaml