These files are GGML format model files for CalderaAI's 13B Ouroboros.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:

These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.

These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.

They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.

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.

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.

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

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

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: Luke from CarbonQuill, Aemon Algiz.

Patreon special mentions: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse

Thank you to all my generous patrons and donaters!

Ouroboros is an experimental model based on Meta's LLaMA [v1] 13B base model using a custom merging technique, tweaking each layer's merge % based on internal tests against the PTB dataset, scoring ~26.31 according to internal evaluation (6 samples, sequence length 1024; this testing is not empirical, it's a quick way to find near-optimum values). Testing, evaluating, and remixing this model is absolutely permissible and even encouraged (within the bounds of Meta's LLaMAv1 license agreement); the more feedback the better we can tune our process! 😊

Ouroboros is comprised of 40 layers [LLaMAv1 13B standard] mixed at optimized ratios VS the PTB dataset for lowest perplexity score. Listed below are the paired models and ratios merged per layer.

Tier One Merge:

13B-airoboros-gpt4-1.4 > 13B-orca_mini_v2

[0.22, 0.85, 0.89, 0.98, 0.3, 0.41, 0.71, 0.83, 0.32, 0.1, 0.44, 0.6, 0.53, 0.15, 0.86, 0.79, 0.93, 0.02, 0.19, 0.82, 0.01, 0.52, 0.07, 0.27, 0.73, 0.86, 0.08, 0.67, 0.42, 0.28, 0.37, 0.08, 0.95, 0.68, 0.45, 0.08, 0.7, 0.93, 0.96, 0.43]

13B-gpt4-x-alpaca > 13B-Vicuna-cocktail

[0.65, 0.94, 0.98, 0.87, 0.28, 0.64, 0.73, 0.7, 0.95, 0.89, 0.84, 0.9, 0.59, 0.92, 0.28, 0.61, 0.88, 0.73, 0.34, 0.85, 0.98, 0.05, 0.74, 0.92, 0.5, 0.78, 0.26, 0.4, 0.27, 0.65, 0.71, 0.7, 0.8, 0.93, 0.36, 0.03, 0.45, 0.39, 0.77, 0.06]

Tier Two Merge:

[13B-airoboros-gpt4-1.4 + 13B-orca_mini_v2] offspring > [13B-gpt4-x-alpaca + 13B-Vicuna-cocktail] offspring

[0.2, 0.83, 0.24, 0.03, 0.37, 0.62, 0.02, 0.82, 0.65, 0.63, 0.45, 0.65, 0.48, 0.45, 0.24, 0.76, 0.06, 0.31, 0.45, 0.86, 0.23, 0.99, 0.93, 0.84, 0.96, 0.53, 0.95, 0.32, 0.19, 0.06, 0.4, 0.08, 0.62, 0.4, 0.26, 0.12, 0.16, 0.91, 0.14, 0.0]

Result:

13B-Ouroboros, a model that seems uncensored and highly competent. So far only Alpaca instruction promting has been tested and seems to work solidly well.

Alpaca's instruct format can be used to do many things, including control of the terms of behavior between a user and a response from an agent in chat. Below is an example of a command injected into memory.

### Instruction:
Make Narrator function as a text based adventure game that responds with verbose, detailed, and creative descriptions of what happens next after Player's response.
Make Player function as the player input for Narrator's text based adventure game, controlling a character named (insert character name here, their short bio, and
whatever quest or other information to keep consistent in the interaction).

### Response:
{an empty new line here}

13B-airoboros-gpt4-1.4 by jondurbin

13B-orca_mini_v2 by psmathur

13B-gpt4-x-alpaca by chavinlo

13B-Vicuna-cocktail by reeducator

Also thanks to Meta for LLaMA.

Each model and LoRA was hand picked and considered for what it could contribute to this ensemble. Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community.