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README.md
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@@ -44,6 +44,8 @@ This repo contains GGML format model files for [NousResearch's Nous Puffin 70B](
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The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.
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### About GGML
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GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration:
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{prompt}
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### RESPONSE:
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```
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<!-- compatibility_ggml start -->
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| [nous-puffin-70b.ggmlv3.Q5_0.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_0.bin) | Q5_0 | 5 | 47.46 GB| 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
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| [nous-puffin-70b.ggmlv3.Q5_K_S.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_K_S.bin) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
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| [nous-puffin-70b.ggmlv3.Q5_K_M.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_K_M.bin) | Q5_K_M | 5 | 48.75 GB| 51.25 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 |
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| nous-puffin-70b.ggmlv3.q5_1.bin | q5_1 | 5 | 51.76 GB | 54.26 GB | Original quant method, 5-bit. Higher accuracy, slower inference than q5_0. |
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| nous-puffin-70b.ggmlv3.q6_K.bin | q6_K | 6 | 56.59 GB | 59.09 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
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| nous-puffin-70b.ggmlv3.q8_0.bin | q8_0 | 8 | 73.23 GB | 75.73 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
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### q5_1, q6_K and q8_0 files require expansion from archive
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**Note
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<details>
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<summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
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### q5_1
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Please download:
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* `nous-puffin-70b.ggmlv3.q5_1.zip`
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* `nous-puffin-70b.ggmlv3.q5_1.z01`
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### q6_K
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Please download:
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* `nous-puffin-70b.ggmlv3.q6_K.zip`
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* `nous-puffin-70b.ggmlv3.q6_K.z01`
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### q8_0
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Please download:
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* `nous-puffin-70b.ggmlv3.q8_0.zip`
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* `nous-puffin-70b.ggmlv3.q8_0.z01`
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Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use `7zip` - the basic `unzip` tool did not work. Example:
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```
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sudo apt update -y && sudo apt install 7zip
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7zz x nous-puffin-70b.ggmlv3.q6_K.zip
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```
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</details>
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## How to run in `llama.cpp`
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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 40 -gqa 8 -m nous-puffin-70b.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### HUMAN:\
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```
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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`. If you are fully offloading the model to GPU, use `-t 1`
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**Special thanks to**: Aemon Algiz.
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**Patreon special mentions**:
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Thank you to all my generous patrons and donaters!
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The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.
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Please use the GGUF models instead.
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### About GGML
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GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration:
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{prompt}
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### RESPONSE:
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```
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<!-- compatibility_ggml start -->
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| [nous-puffin-70b.ggmlv3.Q5_0.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_0.bin) | Q5_0 | 5 | 47.46 GB| 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
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| [nous-puffin-70b.ggmlv3.Q5_K_S.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_K_S.bin) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
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| [nous-puffin-70b.ggmlv3.Q5_K_M.bin](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML/blob/main/nous-puffin-70b.ggmlv3.Q5_K_M.bin) | Q5_K_M | 5 | 48.75 GB| 51.25 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 |
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**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.
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## How to run in `llama.cpp`
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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 40 -gqa 8 -m nous-puffin-70b.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### HUMAN:\n{prompt}\n\n### RESPONSE:"
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```
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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`. If you are fully offloading the model to GPU, use `-t 1`
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**Special thanks to**: Aemon Algiz.
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**Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser
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Thank you to all my generous patrons and donaters!
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