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FPHam's Karen The Editor 13B GGML

These files are GGML format model files for FPHam's Karen The Editor 13B.

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

Repositories available

Compatibility

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

I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit 2d5db48.

They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.

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 only compatible with llama.cpp as of June 6th, commit 2d43387.

They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.

Explanation of the new k-quant methods

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
Karen-The-Editor.ggmlv3.q2_K.bin	q2_K	2	5.43 GB	7.93 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.
Karen-The-Editor.ggmlv3.q3_K_L.bin	q3_K_L	3	6.87 GB	9.37 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
Karen-The-Editor.ggmlv3.q3_K_M.bin	q3_K_M	3	6.25 GB	8.75 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
Karen-The-Editor.ggmlv3.q3_K_S.bin	q3_K_S	3	5.59 GB	8.09 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
Karen-The-Editor.ggmlv3.q4_0.bin	q4_0	4	7.32 GB	9.82 GB	Original llama.cpp quant method, 4-bit.
Karen-The-Editor.ggmlv3.q4_1.bin	q4_1	4	8.14 GB	10.64 GB	Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
Karen-The-Editor.ggmlv3.q4_K_M.bin	q4_K_M	4	7.82 GB	10.32 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
Karen-The-Editor.ggmlv3.q4_K_S.bin	q4_K_S	4	7.32 GB	9.82 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
Karen-The-Editor.ggmlv3.q5_0.bin	q5_0	5	8.95 GB	11.45 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
Karen-The-Editor.ggmlv3.q5_1.bin	q5_1	5	9.76 GB	12.26 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
Karen-The-Editor.ggmlv3.q5_K_M.bin	q5_K_M	5	9.21 GB	11.71 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
Karen-The-Editor.ggmlv3.q5_K_S.bin	q5_K_S	5	8.95 GB	11.45 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
Karen-The-Editor.ggmlv3.q6_K.bin	q6_K	6	10.68 GB	13.18 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
Karen-The-Editor.ggmlv3.q8_0.bin	q8_0	8	13.83 GB	16.33 GB	Original llama.cpp 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 Karen-The-Editor.ggmlv3.q5_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, Dmitriy Samsonov.

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Thank you to all my generous patrons and donaters!

Original model card: FPHam's Karen The Editor 13B

Karen is an editor for your fiction. (v.0.2)

She fixes grammar and wording issues, but doesn't necessary start rewording everything like ChatGPT into a corporate talk. So it should keep the style intact.

Based on LLAMA 13b and Wizard-Vucna-uncensored finetune, then finetuned with about 20k grammar examples (bad grammar/good grammar).

Quantized version (Quantized by TheBloke)

Karen gets triggered by this prompt (pun intended):

USER: Edit the following for spelling and grammar mistakes: 
ASSISTANT:

Feed it a paragraph at a time - that's where she works best - a few sentences.

Here is my instruct yaml for oobabooga webui (in Instruct mode):

user: "USER: Edit the following for spelling and grammar mistakes: "
bot: "ASSISTANT:"
turn_template: "<|user|> <|user-message|>\n<|bot|> <|bot-message|></s>\n"
context: "Assistant is a meticulous senior editor with a specialization in editing fictional stories. When given text, Assistant detects and corrects grammatical errors, including subject-verb agreement, tense consistency, punctuation, capitalization, use of correct articles and correct present perfect and past perfect tense.\n"

I am not sure to what extend the context part has any effect. Doesn't hurt though.

This is a version 0.2 and finetuned just so it retains the underlaying Vicuna qualities.

Example:

Input

USER: Edit the following for spelling and grammar mistakes:
unce upon time lived name John who have pet monkey Jackie.
one day while walkin down the street he saw his friend Tom coming to him.
"Hey Tom" said John "hows it going?"

Tom replyied "Not too bad how 'bout yourself?". as they was talk Jackie sudden jump onto Tom shoulder and started play with his hair.
Tom look at John and ask "Is that yours?"

John reply "Yeah, this crazy lil guy follow me everywhere" just then Jackie grab hold of Tom glasses and try to take thmm off.
Tom struggle to keep his balance as he laugh hystericaly.

Output

ASSISTANT: Once upon a time there lived a man named John who had a pet monkey called Jackie.
One day while walking down the street he saw his friend Tom approaching him.
"Hey Tom," said John. "How's it going?"

Tom replied, "Not too bad, how about yourself?" As they were talking, Jackie suddenly jumped onto Tom's shoulder and began playing with his hair.
Tom looked at John and asked, "Is that yours?"

John replied, "Yeah, this crazy little guy follows me everywhere." Just then Jackie grabbed hold of Tom's glasses and tried to take them off.
Tom struggled to keep his balance as he laughed hysterically.

Goal: to create the best grammar checker you have ever seen

To do:

train on larger dataset, big, enormous, gargantuan
see if finetuning on just plain LLAMA without Vicuna would work better or worse (the theory is that it will be very focused on editing and nothing else)
explore what different settings (temperature, top_p, top_k do for this type of finetune)
create Rachel, the paraphrasing editor