inference: false
license: other
Eric Hartford's WizardLM Uncensored Falcon 40B GGML
These files are GGCC format model files for Eric Hartford's WizardLM Uncensored Falcon 40B.
These files will not work in llama.cpp, text-generation-webui or KoboldCpp.
GGCC is a new format created in a new fork of llama.cpp that introduced this new Falcon GGML-based support: cmp-nc/ggllm.cpp.
Currently these files will also not work with code that previously supported Falcon, such as LoLLMs Web UI and ctransformers. But support should be added soon.
For GGMLv3 files compatible with those UIs, please see the old ggmlv3 branch.
Repositories available
- 4-bit GPTQ model for GPU inference
- 3-bit GPTQ model for GPU inference.
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- Eric's unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Compatibility
To build cmp-nct's fork of llama.cpp with Falcon support plus CUDA acceleration, please try the following steps:
git clone https://github.com/cmp-nct/ggllm.cpp
cd ggllm.cpp
rm -rf build && mkdir build && cd build && cmake -DGGML_CUBLAS=1 .. && cmake --build . --config Release
Compiling on Windows: developer cmp-nct notes: 'I personally compile it using VScode. When compiling with CUDA support using the Microsoft compiler it's essential to select the "Community edition build tools". Otherwise CUDA won't compile.'
Once compiled you can then use bin/falcon_main just like you would use llama.cpp. For example:
bin/falcon_main -t 8 -ngl 100 -b 1 -m falcon7b-instruct.ggmlv3.q4_0.bin -p "What is a falcon?\n### Response:"
You can specify -ngl 100 regardles of your VRAM, as it will automatically detect how much VRAM is available to be used.
Adjust -t 8 (the number of CPU cores to use) according to what performs best on your system. Do not exceed the number of physical CPU cores you have.
-b 1 reduces batch size to 1. This slightly lowers prompt evaluation time, but frees up VRAM to load more of the model on to your GPU. If you find prompt evaluation too slow and have enough spare VRAM, you can remove this parameter.
Please see https://github.com/cmp-nct/ggllm.cpp for further details and instructions.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| wizard-falcon40b.ggccv1.q2_K.bin | q2_K | 2 | 13.74 GB | 16.24 GB | Uses GGML_TYPE_Q2_K for all tensors. |
| wizard-falcon40b.ggccv1.q3_K.bin | q3_K_S | 3 | 17.98 GB | 20.48 GB | Uses GGML_TYPE_Q3_K for all tensors |
| wizard-falcon40b.ggccv1.q4_K.bin | q4_K_S | 4 | 23.54 GB | 26.04 GB | Uses GGML_TYPE_Q4_K for all tensors |
| wizard-falcon40b.ggccv1.q5_K.bin | q5_K_S | 5 | 28.77 GB | 31.27 GB | Uses GGML_TYPE_Q5_K for all tensors |
| wizard-falcon40b.ggccv1.q6_K.bin | q6_K | 6 | 34.33 GB | 36.83 GB | Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| wizard-falcon40b.ggccv1.q8_0.bin | q8_0 | 8 | 44.46 GB | 46.96 GB | 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.
Discord
For further support, and discussions on these models and AI in general, join us at:
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.
Patreon special mentions: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke.
Thank you to all my generous patrons and donaters!
Original model card: Eric Hartford's WizardLM Uncensored Falcon 40B
This is WizardLM trained on top of tiiuae/falcon-40b, with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
Prompt format is WizardLM.
What is a falcon? Can I keep one as a pet?
### Response:
Thank you chirper.ai for sponsoring some of my compute!