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README.md

@@ -55,9 +55,9 @@ 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/CodeLlama-13B-OASST-SFT-v10-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-OASST-SFT-v10-GGUF)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-OASST-SFT-v10-GGML)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML)
 * [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)
 
 ## Prompt template: ChatML
@@ -102,20 +102,20 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q2_K.bin | Q2_K | 2 | 5.74 GB| 8.24 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. |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_S.bin | Q3_K_S | 3 | 5.87 GB| 8.37 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_M.bin | Q3_K_M | 3 | 6.53 GB| 9.03 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 |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_L.bin | Q3_K_L | 3 | 7.14 GB| 9.64 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 |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q4_0.bin | Q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_S.bin | Q4_K_S | 4 | 7.56 GB| 10.06 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_M.bin | Q4_K_M | 4 | 8.06 GB| 10.56 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 |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q4_1.bin | Q4_1 | 4 | 8.14 GB| 10.64 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. |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q5_0.bin | Q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_S.bin | Q5_K_S | 5 | 9.15 GB| 11.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_M.bin | Q5_K_M | 5 | 9.40 GB| 11.90 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 |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q5_1.bin | Q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q6_K.bin | Q6_K | 6 | 10.83 GB| 13.33 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
-| codellama-13b-oasst-sft-v10.ggmlv3.Q8_0.bin | Q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q2_K.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q2_K.bin) | Q2_K | 2 | 5.74 GB| 8.24 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. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_S.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_S.bin) | Q3_K_S | 3 | 5.87 GB| 8.37 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_M.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_M.bin) | Q3_K_M | 3 | 6.53 GB| 9.03 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 |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_L.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q3_K_L.bin) | Q3_K_L | 3 | 7.14 GB| 9.64 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 |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q4_0.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q4_0.bin) | Q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_S.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_S.bin) | Q4_K_S | 4 | 7.56 GB| 10.06 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_M.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q4_K_M.bin) | Q4_K_M | 4 | 8.06 GB| 10.56 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 |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q4_1.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q4_1.bin) | Q4_1 | 4 | 8.14 GB| 10.64 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. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q5_0.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q5_0.bin) | Q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_S.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_S.bin) | Q5_K_S | 5 | 9.15 GB| 11.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_M.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q5_K_M.bin) | Q5_K_M | 5 | 9.40 GB| 11.90 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 |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q5_1.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q5_1.bin) | Q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q6_K.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q6_K.bin) | Q6_K | 6 | 10.83 GB| 13.33 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
+| [codellama-13b-oasst-sft-v10.ggmlv3.Q8_0.bin](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGML/blob/main/codellama-13b-oasst-sft-v10.ggmlv3.Q8_0.bin) | Q8_0 | 8 | 13.83 GB| 16.33 GB | Original 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.
 
@@ -165,7 +165,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 
 **Special thanks to**: Aemon Algiz.
 
-**Patreon special mentions**: Kacper Wikieł, knownsqashed, Leonard Tan, Asp the Wyvern, Daniel P. Andersen, Luke Pendergrass, Stanislav Ovsiannikov, RoA, Dave, Ai Maven, Kalila, Will Dee, Imad Khwaja, Nitin Borwankar, Joseph William Delisle, Tony Hughes, Cory Kujawski, Rishabh Srivastava, Russ Johnson, Stephen Murray, Lone Striker, Johann-Peter Hartmann, Elle, J, Deep Realms, SuperWojo, Raven Klaugh, Sebastain Graf, ReadyPlayerEmma, Alps Aficionado, Mano Prime, Derek Yates, Gabriel Puliatti, Mesiah Bishop, Magnesian, Sean Connelly, biorpg, Iucharbius, Olakabola, Fen Risland, Space Cruiser, theTransient, Illia Dulskyi, Thomas Belote, Spencer Kim, Pieter, John Detwiler, Fred von Graf, Michael Davis, Swaroop Kallakuri, subjectnull, Clay Pascal, Subspace Studios, Chris Smitley, Enrico Ros, usrbinkat, Steven Wood, alfie_i, David Ziegler, Willem Michiel, Matthew Berman, Andrey, Pyrater, Jeffrey Morgan, vamX, LangChain4j, Luke @flexchar, Trenton Dambrowitz, Pierre Kircher, Alex, Sam, James Bentley, Edmond Seymore, Eugene Pentland, Pedro Madruga, Rainer Wilmers, Dan Guido, Nathan LeClaire, Spiking Neurons AB, Talal Aujan, zynix, Artur Olbinski, Michael Levine, 阿明, K, John Villwock, Nikolai Manek, Femi Adebogun, senxiiz, Deo Leter, NimbleBox.ai, Viktor Bowallius, Geoffrey Montalvo, Mandus, Ajan Kanaga, ya boyyy, Jonathan Leane, webtim, Brandon Frisco, danny, Alexandros Triantafyllidis, Gabriel Tamborski, Randy H, terasurfer, Vadim, Junyu Yang, Vitor Caleffi, Chadd, transmissions 11
+**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!