Upload README.md

README.md

@@ -38,6 +38,13 @@ quantized_by: TheBloke
 
 This repo contains GGML format model files for [Together's Llama2 7B 32K Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct).
 
+### Important note regarding GGML files.
+
+The GGML format has now been superseded by GGUF.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.
+
+Please use the GGUF models instead.
+### About GGML
+
 GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
 * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
 * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
@@ -49,7 +56,8 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 ## Repositories available
 
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML)
 * [Together's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct)
 
 ## Prompt template: Llama2-Instruct-Only
@@ -58,14 +66,19 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 [INST]
 {prompt}
 [\INST]
+
 ```
 
 <!-- compatibility_ggml start -->
 ## Compatibility
 
-These quantised GGML files are compatible with llama.cpp as of June 6th, commit `2d43387`.
+These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.
+
+For support with latest llama.cpp, please use GGUF files instead.
 
-They should also be compatible with all UIs, libraries and utilities which use GGML.
+The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
+
+As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.
 
 ## Explanation of the new k-quant methods
 <details>
@@ -88,17 +101,17 @@ Refer to the Provided Files table below to see what files use which methods, and
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [llama-2-7b-32k-instruct.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q2_K.bin) | q2_K | 2 | 2.87 GB| 5.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
-| [llama-2-7b-32k-instruct.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 3.60 GB| 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
-| [llama-2-7b-32k-instruct.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 3.28 GB| 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
 | [llama-2-7b-32k-instruct.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 2.95 GB| 5.45 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| [llama-2-7b-32k-instruct.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 3.28 GB| 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
+| [llama-2-7b-32k-instruct.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 3.60 GB| 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
 | [llama-2-7b-32k-instruct.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_0.bin) | q4_0 | 4 | 3.83 GB| 6.33 GB | Original quant method, 4-bit. |
-| [llama-2-7b-32k-instruct.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_1.bin) | q4_1 | 4 | 4.24 GB| 6.74 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
-| [llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 4.08 GB| 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
 | [llama-2-7b-32k-instruct.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 3.83 GB| 6.33 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| [llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 4.08 GB| 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
+| [llama-2-7b-32k-instruct.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q4_1.bin) | q4_1 | 4 | 4.24 GB| 6.74 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
 | [llama-2-7b-32k-instruct.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_0.bin) | q5_0 | 5 | 4.65 GB| 7.15 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
-| [llama-2-7b-32k-instruct.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_1.bin) | q5_1 | 5 | 5.06 GB| 7.56 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
-| [llama-2-7b-32k-instruct.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 4.78 GB| 7.28 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 |
 | [llama-2-7b-32k-instruct.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 4.65 GB| 7.15 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| [llama-2-7b-32k-instruct.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 4.78 GB| 7.28 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 |
+| [llama-2-7b-32k-instruct.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q5_1.bin) | q5_1 | 5 | 5.06 GB| 7.56 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
 | [llama-2-7b-32k-instruct.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q6_K.bin) | q6_K | 6 | 5.53 GB| 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
 | [llama-2-7b-32k-instruct.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/Llama-2-7B-32K-Instruct-GGML/blob/main/llama-2-7b-32k-instruct.ggmlv3.q8_0.bin) | q8_0 | 8 | 7.13 GB| 9.63 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
 
@@ -106,10 +119,12 @@ Refer to the Provided Files table below to see what files use which methods, and
 
 ## How to run in `llama.cpp`
 
-I use the following command line; adjust for your tastes and needs:
+Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.
+
+For compatibility with latest llama.cpp, please use GGUF files instead.
 
 ```
-./main -t 10 -ngl 32 -m llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
+./main -t 10 -ngl 32 -m llama-2-7b-32k-instruct.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST]\n{prompt}\n[\INST]"
 ```
 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`.
 
@@ -148,7 +163,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 
 **Special thanks to**: Aemon Algiz.
 
-**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
+**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
 
 
 Thank you to all my generous patrons and donaters!