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Nomic.ai's GPT4All Snoozy 13B GGML

These files are GGML format model files for Nomic.ai's GPT4All Snoozy 13B.

These are SuperHOT GGMLs with an increased context length. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. It was discovered and developed by kaiokendev.

In order to use the increased context length, you can presently use:

• KoboldCpp - release 1.33 or later.

Support is also expected to come to llama.cpp, however it is still being worked on and there is currently no ETA for that.

To use the increased context with KoboldCpp and (when supported) llama.cpp, simply use --contextsize to set the desired context, eg --contextsize 4096 or --contextsize 8192.

Repositories available

• 4-bit GPTQ models for GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference
• Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions
• Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions

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Compatibility

These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants.

However the increased context length won't work without specific support. See the note in the introduction for details on using increased context.

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. <!-- compatibility_ggml end -->

Provided files

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 koboldcpp

On Linux I use the following command line to launch the KoboldCpp UI with OpenCL aceleration and a context size of 4096:

python ./koboldcpp.py --stream --unbantokens --threads 8 --usecublas 100 gpt4all-snoozy-13b-superhot-8k.ggmlv3.q5_0.bin

Change --gpulayers 100 to the number of layers you want/are able to offload to the GPU. Remove it if you don't have GPU acceleration.

For OpenCL acceleration, change --usecublas to --useclblast 0 0. You may need to change the second 0 to 1 if you have both an iGPU and a discrete GPU.

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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
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

Patreon special mentions: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.

Thank you to all my generous patrons and donaters!

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Original model card: Kaio Ken's SuperHOT 8K

SuperHOT Prototype 2 w/ 8K Context

This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in the github blog. Tests have shown that the model does indeed leverage the extended context at 8K.

You will need to use either the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192

Looking for Merged & Quantized Models?

• 30B 4-bit CUDA: tmpupload/superhot-30b-8k-4bit-safetensors
• 30B 4-bit CUDA 128g: tmpupload/superhot-30b-8k-4bit-128g-safetensors

Training Details

I trained the LoRA with the following configuration:

• 1200 samples (~400 samples over 2048 sequence length)
• learning rate of 3e-4
• 3 epochs
• The exported modules are:
  • q_proj
  • k_proj
  • v_proj
  • o_proj
  • no bias
• Rank = 4
• Alpha = 8
• no dropout
• weight decay of 0.1
• AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
• Trained on 4-bit base model

Original model card: Nomic.ai's GPT4All Snoozy 13B

Model Card for GPT4All-13b-snoozy

A GPL licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

This model has been finetuned from LLama 13B

• Developed by: Nomic AI
• Model Type: A finetuned LLama 13B model on assistant style interaction data
• Language(s) (NLP): English
• License: GPL
• Finetuned from model [optional]: LLama 13B

This model was trained on nomic-ai/gpt4all-j-prompt-generations using revision=v1.3-groovy

Model Sources [optional]

<!-- Provide the basic links for the model. -->

• Repository: https://github.com/nomic-ai/gpt4all
• Base Model Repository: https://github.com/facebookresearch/llama
• Demo [optional]: https://gpt4all.io/

Results

Results on common sense reasoning benchmarks

| Model                     |  BoolQ   |   PIQA   | HellaSwag | WinoGrande |  ARC-e   |  ARC-c   |   OBQA   |   Avg.   |
|:--------------------------|:--------:|:--------:|:---------:|:----------:|:--------:|:--------:|:--------:|:--------:|
| GPT4All-J 6B v1.0         |   73.4   |   74.8   |   63.4    |    64.7    |   54.9   |   36.0   |   40.2   |   58.2   |
| GPT4All-J v1.1-breezy     |   74.0   |   75.1   |   63.2    |    63.6    |   55.4   |   34.9   |   38.4   |   57.8   |
| GPT4All-J v1.2-jazzy      |   74.8   |   74.9   |   63.6    |    63.8    |   56.6   |   35.3   |   41.0   |   58.6   |
| GPT4All-J v1.3-groovy     |   73.6   |   74.3   |   63.8    |    63.5    |   57.7   |   35.0   |   38.8   |   58.1   |
| GPT4All-J Lora 6B         |   68.6   |   75.8   |   66.2    |    63.5    |   56.4   |   35.7   |   40.2   |   58.1   |
| GPT4All LLaMa Lora 7B     |   73.1   |   77.6   |   72.1    |    67.8    |   51.1   |   40.4   |   40.2   |   60.3   |
| GPT4All 13B snoozy        | **83.3** |   79.2   |   75.0    |  **71.3**  |   60.9   |   44.2   |   43.4   | **65.3** |
| Dolly 6B                  |   68.8   |   77.3   |   67.6    |    63.9    |   62.9   |   38.7   |   41.2   |   60.1   |
| Dolly 12B                 |   56.7   |   75.4   |   71.0    |    62.2    |   64.6   |   38.5   |   40.4   |   58.4   |
| Alpaca 7B                 |   73.9   |   77.2   |   73.9    |    66.1    |   59.8   |   43.3   |   43.4   |   62.4   |
| Alpaca Lora 7B            |   74.3   | **79.3** |   74.0    |    68.8    |   56.6   |   43.9   |   42.6   |   62.8   |
| GPT-J 6.7B                |   65.4   |   76.2   |   66.2    |    64.1    |   62.2   |   36.6   |   38.2   |   58.4   |
| LLama 7B                  |   73.1   |   77.4   |   73.0    |    66.9    |   52.5   |   41.4   |   42.4   |   61.0   |
| LLama 13B                 |   68.5   |   79.1   |   76.2    |    70.1    |   60.0   | **44.6** |   42.2   |   63.0   |
| Pythia 6.7B               |   63.5   |   76.3   |   64.0    |    61.1    |   61.3   |   35.2   |   37.2   |   57.0   |
| Pythia 12B                |   67.7   |   76.6   |   67.3    |    63.8    |   63.9   |   34.8   |    38    |   58.9   |
| Fastchat T5               |   81.5   |   64.6   |   46.3    |    61.8    |   49.3   |   33.3   |   39.4   |   53.7   |
| Fastchat Vicuña 7B        |   76.6   |   77.2   |   70.7    |    67.3    |   53.5   |   41.2   |   40.8   |   61.0   |
| Fastchat Vicuña 13B       |   81.5   |   76.8   |   73.3    |    66.7    |   57.4   |   42.7   |   43.6   |   63.1   |
| StableVicuña RLHF         |   82.3   |   78.6   |   74.1    |    70.9    |   61.0   |   43.5   | **44.4** |   65.0   |
| StableLM Tuned            |   62.5   |   71.2   |   53.6    |    54.8    |   52.4   |   31.1   |   33.4   |   51.3   |
| StableLM Base             |   60.1   |   67.4   |   41.2    |    50.1    |   44.9   |   27.0   |   32.0   |   42.2   |
| Koala 13B                 |   76.5   |   77.9   |   72.6    |    68.8    |   54.3   |   41.0   |   42.8   |   62.0   |
| Open Assistant Pythia 12B |   67.9   |   78.0   |   68.1    |    65.0    |   64.2   |   40.4   |   43.2   |   61.0   |
| Mosaic mpt-7B              |   74.8   | **79.3** | **76.3**  |    68.6    | **70.0** |   42.2   |   42.6   |   64.8   |
| text-davinci-003          |   88.1   |   83.8   |   83.4    |    75.8    |   83.9   |   63.9   |   51.0   |   75.7   |