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CalderaAI's 30B Epsilon GGML

These files are GGML format model files for CalderaAI's 30B Epsilon.

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

KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
LoLLMS Web UI, a great web UI with GPU acceleration via the c_transformers backend.
LM Studio, a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
text-generation-webui, the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
ctransformers, a Python library with LangChain support and OpenAI-compatible AI server.
llama-cpp-python, a Python library with OpenAI-compatible API server.

Repositories available

Prompt template: Alpaca

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

### Instruction: {prompt}

### Response:

Compatibility

Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`

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 They may be phased out soon, as they are largely superseded by the new k-quant methods.

New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`

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.

Explanation of the new k-quant 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
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
30b-epsilon.ggmlv3.q2_K.bin	q2_K	2	13.71 GB	16.21 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
30b-epsilon.ggmlv3.q3_K_L.bin	q3_K_L	3	17.28 GB	19.78 GB	New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
30b-epsilon.ggmlv3.q3_K_M.bin	q3_K_M	3	15.72 GB	18.22 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
30b-epsilon.ggmlv3.q3_K_S.bin	q3_K_S	3	14.06 GB	16.56 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
30b-epsilon.ggmlv3.q4_0.bin	q4_0	4	18.30 GB	20.80 GB	Original quant method, 4-bit.
30b-epsilon.ggmlv3.q4_1.bin	q4_1	4	20.33 GB	22.83 GB	Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
30b-epsilon.ggmlv3.q4_K_M.bin	q4_K_M	4	19.62 GB	22.12 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
30b-epsilon.ggmlv3.q4_K_S.bin	q4_K_S	4	18.36 GB	20.86 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
30b-epsilon.ggmlv3.q5_0.bin	q5_0	5	22.37 GB	24.87 GB	Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
30b-epsilon.ggmlv3.q5_1.bin	q5_1	5	24.40 GB	26.90 GB	Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
30b-epsilon.ggmlv3.q5_K_M.bin	q5_K_M	5	23.05 GB	25.55 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
30b-epsilon.ggmlv3.q5_K_S.bin	q5_K_S	5	22.40 GB	24.90 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
30b-epsilon.ggmlv3.q6_K.bin	q6_K	6	26.69 GB	29.19 GB	New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
30b-epsilon.ggmlv3.q8_0.bin	q8_0	8	34.56 GB	37.06 GB	Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

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.

How to run in `llama.cpp`

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

./main -t 10 -ngl 32 -m 30b-epsilon.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"

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

How to run in `text-generation-webui`

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

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!

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
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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!

Original model card: CalderaAI's 30B Epsilon

30B-Epsilon

Epsilon is an instruct based general purpose model assembled from hand picked models and LoRAs. There is no censorship and it follows instructions in the Alpaca format. This means you can create your own rules in the context memory of your inference system of choice [mainly KoboldAI or Text Generation Webui and chat UIs like SillyTavern and so on].

Composition:

This model is the result of an experimental use of LoRAs on language models and model merges. [] = applied as LoRA to a composite model | () = combined as composite models 30B-Epsilon = [SuperCOT[SuperHOT-prototype13b-8192[(wizardlmuncensored+((hippogriff+manticore)+(StoryV2))]

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}

All datasets from all models and LoRAs used were documented and reviewed as model candidates for merging. Model candidates were based on five core principles: creativity, logic, inference, instruction following, and longevity of trained responses. SuperHOT-prototype30b-8192 was used in this mix, not the 8K version; the prototype LoRA seems to have been removed [from HF] as of this writing. The GPT4Alpaca LoRA from Chansung was removed from this amalgam following a thorough review of where censorship and railroading the user came from in 33B-Lazarus. This is not a reflection of ChanSung's excellent work - it merely did not fit the purpose of this model.

Language Models and LoRAs Used Credits:

manticore-30b-chat-pyg-alpha [Epoch0.4] by openaccess-ai-collective

https://huggingface.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha

hippogriff-30b-chat by openaccess-ai-collective

https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat

WizardLM-33B-V1.0-Uncensored by ehartford

https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored

Storytelling-LLaMa-LoRA [30B, Version 2] by GamerUnTouch

https://huggingface.co/GamerUntouch/Storytelling-LLaMa-LoRAs

SuperCOT-LoRA [30B] by kaiokendev

https://huggingface.co/kaiokendev/SuperCOT-LoRA

SuperHOT-LoRA-prototype30b-8192 [30b, not 8K version, but a removed prototype] by kaiokendev

https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test [Similar LoRA to one since removed that was used in making this model.]

Also thanks to Meta for LLaMA and to each and every one of you who developed these fine-tunes and LoRAs.