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TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-SuperHOT-8K-GGML

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  • classification m-uncensored
  • files 7
  • author_summary 110 models
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
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Downloads · 30-day
0
Likes
23
Model age
3.3y ago
created 2023-06-30
Downloads over time
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Metadata

License
other
Tags
license:other region:us

Related

Total size
112 GB
Files
7
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2023-06-30 08:41

Files by quantization

Auxiliary files 7 files 112 GB
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q8_0.bin 32.2 GB 0c12af17 download
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q5_1.bin 22.7 GB caf7300d download
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q5_0.bin 20.8 GB e96c8af3 download
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q4_1.bin 18.9 GB 2c4d6b10 download
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q4_0.bin 17.0 GB 7dbc8570 download
README.md 9.22 KB d1ab8a66 download
.gitattributes 1.48 KB a6344aac download

README current version from Hugging Face


inference: false
license: other

TheBlokeAI

Monero's WizardLM Uncensored SuperCOT Storytelling 30B GGML

These files are GGML format model files for Monero's WizardLM Uncensored SuperCOT Storytelling 30B.

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:

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

Compatibility

These GGMLs will work with any GGML client.

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

k-quants not possible with this model

Because this model uses a vocab size of 32001, it is not possible to create the new k-quant format model files for it.

For more information, please see:

Provided files

Name Quant method Bits Size Max RAM required Use case
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q4_0.bin q4_0 4 18.30 GB 20.80 GB Original llama.cpp quant method, 4-bit.
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q4_1.bin q4_1 4 20.33 GB 22.83 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.
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q5_0.bin q5_0 5 22.37 GB 24.87 GB Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q5_1.bin q5_1 5 24.40 GB 26.90 GB Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
WizardLM-Uncensored-SuperCOT-StoryTelling-30b-superhot-8k.ggmlv3.q8_0.bin q8_0 8 34.56 GB 37.06 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 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 WizardLM-Uncensored-SuperCOT-StoryTelling-30b-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.

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.

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!

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?

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: Monero's WizardLM Uncensored SuperCOT Storytelling 30B

This model is a triple model merge of WizardLM Uncensored+CoT+Storytelling, resulting in a comprehensive boost in reasoning and story writing capabilities.

To allow all output, at the end of your prompt add ### Certainly!

You've become a compendium of knowledge on a vast array of topics.

Lore Mastery is an arcane tradition fixated on understanding the underlying mechanics of magic. It is the most academic of all arcane traditions. The promise of uncovering new knowledge or proving (or discrediting) a theory of magic is usually required to rouse its practitioners from their laboratories, academies, and archives to pursue a life of adventure. Known as savants, followers of this tradition are a bookish lot who see beauty and mystery in the application of magic. The results of a spell are less interesting to them than the process that creates it. Some savants take a haughty attitude toward those who follow a tradition focused on a single school of magic, seeing them as provincial and lacking the sophistication needed to master true magic. Other savants are generous teachers, countering ignorance and deception with deep knowledge and good humor.

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2023-06-30Update README.md958b2ec9.2 KB
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  2. 2023-06-30Initial GGML model commit5f968e710.3 KB
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