Upload README.md

README.md

@@ -11,17 +11,20 @@ quantized_by: TheBloke
 ---
 
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-    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+<!-- 200823 -->
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+<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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     <div style="display: flex; flex-direction: column; align-items: flex-start;">
-        <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
     </div>
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-        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
     </div>
 </div>
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
 
 # LosslessMegaCoder Llama2 13B Mini - GGML
@@ -32,6 +35,13 @@ quantized_by: TheBloke
 
 This repo contains GGML format model files for [Rombo Dawg's LosslessMegaCoder Llama2 13B Mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini).
 
+### 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+The GGML format has now been superseded by GGUF.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.
@@ -43,7 +53,8 @@ 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/LosslessMegaCoder-Llama2-13B-Mini-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML)
 * [Rombo Dawg's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini)
 
 ## Prompt template: ChatML
@@ -54,14 +65,19 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 <|im_start|>user
 {prompt}<|im_end|>
 <|im_start|>assistant
+
 ```
 
 <!-- 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.
+
+The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
 
-They should also be compatible with all UIs, libraries and utilities which use GGML.
+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>
@@ -84,17 +100,17 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
-| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 9.14 GB| 11.64 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
+| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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 |
 | [losslessmegacoder-llama2-13b-min.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.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. |
 
@@ -102,10 +118,12 @@ 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 losslessmegacoder-llama2-13b-min.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 losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
 ```
 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`.
 
@@ -122,6 +140,7 @@ For other parameters and how to use them, please refer to [the llama.cpp documen
 Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
 
 <!-- footer start -->
+<!-- 200823 -->
 ## Discord
 
 For further support, and discussions on these models and AI in general, join us at:
@@ -141,18 +160,24 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 * Patreon: https://patreon.com/TheBlokeAI
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
 
-**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+**Special thanks to**: Aemon Algiz.
 
-**Patreon special mentions**: Ajan Kanaga, David Ziegler, Raymond Fosdick, SuperWojo, Sam, webtim, Steven Wood, knownsqashed, Tony Hughes, Junyu Yang, J, Olakabola, Dan Guido, Stephen Murray, John Villwock, vamX, William Sang, Sean Connelly, LangChain4j, Olusegun Samson, Fen Risland, Derek Yates, Karl Bernard, transmissions 11, Trenton Dambrowitz, Pieter, Preetika Verma, Swaroop Kallakuri, Andrey, Slarti, Jonathan Leane, Michael Levine, Kalila, Joseph William Delisle, Rishabh Srivastava, Deo Leter, Luke Pendergrass, Spencer Kim, Geoffrey Montalvo, Thomas Belote, Jeffrey Morgan, Mandus, ya boyyy, Matthew Berman, Magnesian, Ai Maven, senxiiz, Alps Aficionado, Luke @flexchar, Raven Klaugh, Imad Khwaja, Gabriel Puliatti, Johann-Peter Hartmann, usrbinkat, Spiking Neurons AB, Artur Olbinski, chris gileta, danny, Willem Michiel, WelcomeToTheClub, Deep Realms, alfie_i, Dave, Leonard Tan, NimbleBox.ai, Randy H, Daniel P. Andersen, Pyrater, Will Dee, Elle, Space Cruiser, Gabriel Tamborski, Asp the Wyvern, Illia Dulskyi, Nikolai Manek, Sid, Brandon Frisco, Nathan LeClaire, Edmond Seymore, Enrico Ros, Pedro Madruga, Eugene Pentland, John Detwiler, Mano Prime, Stanislav Ovsiannikov, Alex, Vitor Caleffi, K, biorpg, Michael Davis, Lone Striker, Pierre Kircher, theTransient, Fred von Graf, Sebastain Graf, Vadim, Iucharbius, Clay Pascal, Chadd, Mesiah Bishop, terasurfer, Rainer Wilmers, Alexandros Triantafyllidis, Stefan Sabev, Talal Aujan, Cory Kujawski, Viktor Bowallius, subjectnull, ReadyPlayerEmma, zynix
+**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!
 
+And thank you again to a16z for their generous grant.
+
 <!-- footer end -->
 
 # Original model card: Rombo Dawg's LosslessMegaCoder Llama2 13B Mini
 
-This is one of the first models trained on the LosslessMegaCodeTrainingV2_1m_Evol_Uncensored dataset. The version of the dataset used for this model was poorly filtered on some loose parameters that arent anything to write home about but plans for much more refined filtering are in the works
+___________________________
+- Please note this model was not trained on the rombodawg/LosslessMegaCodeTrainingV3_MINI dataset, despite the name similarity. You can find the training data at the bottom of the model card labeled (megacode2-min100)
+___________________________
+
+This is one of the first models trained on the LosslessMegaCodeTrainingV2_1m_Evol_Uncensored dataset. The version of the dataset used for this model was filtered by removed any data with less than 100 tokens but plans for much more refined filtering are in the works
 
 - This model was made as a colaboration between me and andreaskoepf who is an affiliate of Open Assistant.
 
@@ -178,13 +203,13 @@ Gpt4all template:
 - System prompt
 ```
 <|im_start|>system
-{system message}
+"Below is an instruction that describes a task. Write a response that appropriately completes the request."
 ```
 - Prompt template
 ```
 <|im_end|>
 <|im_start|>user
-%1<|im_end|>
+"%1"<|im_end|>
 <|im_start|>assistant
 ```
 
@@ -209,6 +234,10 @@ Oobagooba Text-Generation-Webui Template
   Below is an instruction that describes a task. Write a response that appropriately completes the request.<|im_end|>
 ```
 
+Current quantizations available:
+
+- https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GPTQ
+
 Training data:
 
 - https://wandb.ai/open-assistant/epfl-mt-sft/runs/run34_megacode2_min100_13b
@@ -223,4 +252,4 @@ Link for the filtered dataset used to make this model are bellow:
 
 The original posting for this model was uploaded at the link bellow.
 
-- https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100
+- https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100