inference: false
license: other
Eric Hartford's WizardLM Uncensored Falcon 40B GGML
These files are experimental GGML format model files for Eric Hartford's WizardLM Uncensored Falcon 40B.
These GGML files will not work in llama.cpp, and at the time of writing they will not work with any UI or library. They cannot be used from Python code.
They can be used with a new fork of llama.cpp that adds Falcon GGML support: cmp-nc/ggllm.cpp
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 40B support plus preliminary CUDA acceleration, please try the following steps:
git clone https://github.com/cmp-nct/ggllm.cpp
cd ggllm.cpp
git checkout cuda-integration
rm -rf build && mkdir build && cd build && cmake -DGGML_CUBLAS=1 .. && cmake --build . --config Release
Note from developer cmp-nct for Windows users: '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 -m /workspace/wizard-falcon40b.ggmlv3.q3_K_S.bin -p "What is a falcon?\n### Response:"
Using -ngl 100 will offload all layers to GPU. If you do not have enough VRAM for this, either lower the number or try a smaller quant size as otherwise performance will be severely affected.
Adjust -t 8 according to what performs best on your system. Do not exceed the number of physical CPU cores you have.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| wizard-falcon40b.ggmlv3.q2_K.bin | q2_K | 2 | 13.74 GB | 16.24 GB | Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| wizard-falcon40b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.98 GB | 20.48 GB | Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| wizard-falcon40b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 17.98 GB | 20.48 GB | Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| wizard-falcon40b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 17.98 GB | 20.48 GB | Uses GGML_TYPE_Q3_K for all tensors |
| wizard-falcon40b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 23.54 GB | 26.04 GB | Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| wizard-falcon40b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 23.54 GB | 26.04 GB | Uses GGML_TYPE_Q4_K for all tensors |
| wizard-falcon40b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 28.77 GB | 31.27 GB | Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| wizard-falcon40b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 28.77 GB | 31.27 GB | Uses GGML_TYPE_Q5_K for all tensors |
| wizard-falcon40b.ggmlv3.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.ggmlv3.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.
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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!