Upload README.md

README.md

@@ -1,29 +1,35 @@
 ---
 datasets:
 - garage-bAInd/Open-Platypus
+- Open-Orca/OpenOrca
 inference: false
 language:
 - en
-license: other
+library_name: transformers
+license: llama2
 model_creator: Open-Orca
 model_link: https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B
 model_name: OpenOrca Platypus2 13B
 model_type: llama
+pipeline_tag: text-generation
 quantized_by: TheBloke
 ---
 
 <!-- header start -->
-<div style="width: 100%;">
-    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
 </div>
 <div style="display: flex; justify-content: space-between; width: 100%;">
     <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>
     <div style="display: flex; flex-direction: column; align-items: flex-end;">
-        <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 -->
 
 # OpenOrca Platypus2 13B - GGML
@@ -34,6 +40,13 @@ quantized_by: TheBloke
 
 This repo contains GGML format model files for [Open-Orca's OpenOrca Platypus2 13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B).
 
+### 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+The GGML format has now been superseded by GGUF.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.
@@ -45,7 +58,8 @@ 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/OpenOrca-Platypus2-13B-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML)
 * [Open-Orca's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
 
 ## Prompt template: Alpaca-InstructOnly
@@ -56,14 +70,19 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 {prompt}
 
 ### Response:
+
 ```
 
 <!-- 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>
@@ -86,17 +105,17 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [openorca-platypus2-13b.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
-| [openorca-platypus2-13b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
-| [openorca-platypus2-13b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
 | [openorca-platypus2-13b.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
 | [openorca-platypus2-13b.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
-| [openorca-platypus2-13b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
-| [openorca-platypus2-13b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
 | [openorca-platypus2-13b.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
 | [openorca-platypus2-13b.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
-| [openorca-platypus2-13b.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
-| [openorca-platypus2-13b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
 | [openorca-platypus2-13b.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
+| [openorca-platypus2-13b.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
 | [openorca-platypus2-13b.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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 |
 | [openorca-platypus2-13b.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GGML/blob/main/openorca-platypus2-13b.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. |
 
@@ -104,10 +123,12 @@ 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 openorca-platypus2-13b.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 openorca-platypus2-13b.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction:\n\nWrite a story about llamas\n\n### Response:"
 ```
 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`.
 
@@ -121,9 +142,10 @@ For other parameters and how to use them, please refer to [the llama.cpp documen
 
 ## How to run in `text-generation-webui`
 
-Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
+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:
@@ -143,13 +165,15 @@ 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: Open-Orca's OpenOrca Platypus2 13B
@@ -164,12 +188,28 @@ Thank you to all my generous patrons and donaters!
 
 OpenOrca-Platypus2-13B is a merge of [`garage-bAInd/Platypus2-13B`](https://huggingface.co/garage-bAInd/Platypus2-13B) and [`Open-Orca/OpenOrcaxOpenChat-Preview2-13B`](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B).
 
-This model is more than the sum of its parts! We are happy to be teaming up with the Platypus team to bring you a new model which once again tops the leaderboards!
+This model is more than the sum of its parts! We are happy to be teaming up with the [Platypus](https://platypus-llm.github.io/) team to bring you a new model which once again tops the leaderboards!
+
+Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
+
+
+[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
+
+
+We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.
+
+We will also give sneak-peak announcements on our Discord, which you can find here:
+
+https://AlignmentLab.ai
+
+# Evaluation
+
+## HuggingFace Leaderboard Performance
 
+![HF Leaderboard](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B/resolve/main/Images/OrcaPlatypus13BHFLeaderboard.webp)
 
-# Benchmark Metrics
 
-| Metric                | Value |
+| Metric | Value |
 |-----------------------|-------|
 | MMLU (5-shot)         | 59.5  |
 | ARC (25-shot)         | 62.88 |
@@ -177,19 +217,42 @@ This model is more than the sum of its parts! We are happy to be teaming up with
 | TruthfulQA (0-shot)   | 52.69 |
 | Avg.                  | 64.56 |
 
-We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
+We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.
+
+Please see below for detailed instructions on reproducing benchmark results.
+
+
+## AGIEval Performance
+
+We compare our results to our base Preview2 model (using LM Evaluation Harness).
+
+We find **112%** of the base model's performance on AGI Eval, averaging **0.463**.
+A large part of this boost is the substantial improvement to LSAT Logical Reasoning performance.
+
+![OpenOrca-Platypus2-13B AGIEval Performance](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B/resolve/main/Images/OrcaPlatypus13BAGIEval.webp "AGIEval Performance")
+
+## BigBench-Hard Performance
+
+We compare our results to our base Preview2 model (using LM Evaluation Harness).
+
+We find **105%** of the base model's performance on BigBench-Hard, averaging **0.442**.
+
+![OpenOrca-Platypus2-13B BigBench-Hard Performance](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B/resolve/main/Images/OrcaPlatypus13BBigBenchHard.webp "BigBench-Hard Performance")
 
 
 # Model Details
 
 * **Trained by**: **Platypus2-13B** trained by Cole Hunter & Ariel Lee; **OpenOrcaxOpenChat-Preview2-13B** trained by Open-Orca
-* **Model type:**  **OpenOrca-Platypus2-13B** is an auto-regressive language model based on the LLaMA 2 transformer architecture.
+* **Model type:**  **OpenOrca-Platypus2-13B** is an auto-regressive language model based on the Lllama 2 transformer architecture.
 * **Language(s)**: English
 * **License for Platypus2-13B base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
-* **License for OpenOrcaxOpenChat-Preview2-13B base weights**: LLaMa-2 commercial
+* **License for OpenOrcaxOpenChat-Preview2-13B base weights**: Llama 2 Commercial
+
+
+# Prompting
 
+## Prompt Template for base Platypus2-13B
 
-# Prompt Template for base Platypus2-13B
 ```
 ### Instruction:
 
@@ -199,26 +262,31 @@ We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-eval
 ```
 
 
-# Prompt Template for base OpenOrcaxOpenChat-Preview2-13B
+## Prompt Template for base OpenOrcaxOpenChat-Preview2-13B
 
 OpenChat Llama2 V1: see [OpenOrcaxOpenChat-Preview2-13B](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B) for additional information. 
 
 
-# Training Datasets
+# Training
+
+## Training Datasets
 
 `garage-bAInd/Platypus2-13B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
 
-Please see our [paper](https://platypus-llm.github.io/Platypus.pdf) and [project webpage](https://platypus-llm.github.io) for additional information.
+Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.
+
+`Open-Orca/OpenOrcaxOpenChat-Preview2-13B` trained using a refined subset of most of the GPT-4 data from the [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca).
 
-[`Open-Orca/OpenOrcaxOpenChat-Preview2-13B`] trained using a refined subset of most of the GPT-4 data from the [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca).
 
+## Training Procedure
 
-# Training Procedure
+`Open-Orca/Platypus2-13B` was instruction fine-tuned using LoRA on 1x A100-80GB.
+For training details and inference instructions please see the [Platypus](https://github.com/arielnlee/Platypus) GitHub repo.
 
-`Open-Orca/Platypus2-13B` was instruction fine-tuned using LoRA on 1 A100 80GB. For training details and inference instructions please see the [Platypus](https://github.com/arielnlee/Platypus) GitHub repo.
 
+# Supplemental
 
-# Reproducing Evaluation Results
+## Reproducing Evaluation Results (for HuggingFace Leaderboard Eval)
 
 Install LM Evaluation Harness:
 ```
@@ -231,7 +299,7 @@ git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
 # install
 pip install -e .
 ```
-Each task was evaluated on a single A100 80GB GPU.
+Each task was evaluated on a single A100-80GB GPU.
 
 ARC:
 ```
@@ -254,7 +322,7 @@ python main.py --model hf-causal-experimental --model_args pretrained=Open-Orca/
 ```
 
 
-# Limitations and bias
+## Limitations and bias
 
 Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
@@ -264,22 +332,20 @@ Please see the Responsible Use Guide available at https://ai.meta.com/llama/resp
 # Citations
 
 ```bibtex
-@misc{touvron2023llama,
-    title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
-    author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
-    year={2023},
-    eprint= arXiv 2307.09288
+@software{hunterlee2023orcaplaty1
+  title = {OpenOrcaPlatypus: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset and Merged with divergent STEM and Logic Dataset Model},
+  author = {Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz and Bleys Goodson and Wing Lian and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B},
 }
-```
-```bibtex
-@article{hu2021lora,
-  title={LoRA: Low-Rank Adaptation of Large Language Models},
-  author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
-  journal={CoRR},
-  year={2021}
+@article{platypus2023,
+    title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, 
+    author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
+    booktitle={arXiv preprint arxiv:2308.07317},
+    year={2023}
 }
-```
-```bibtex
 @software{OpenOrcaxOpenChatPreview2,
   title = {OpenOrcaxOpenChatPreview2: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
   author = {Guan Wang and Bleys Goodson and Wing Lian and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
@@ -288,8 +354,6 @@ Please see the Responsible Use Guide available at https://ai.meta.com/llama/resp
   journal = {HuggingFace repository},
   howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B},
 }
-```
-```bibtex
 @software{openchat,
   title = {{OpenChat: Advancing Open-source Language Models with Imperfect Data}},
   author = {Wang, Guan and Cheng, Sijie and Yu, Qiying and Liu, Changling},
@@ -299,4 +363,32 @@ Please see the Responsible Use Guide available at https://ai.meta.com/llama/resp
   year = {2023},
   month = {7},
 }
+@misc{mukherjee2023orca,
+      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
+      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
+      year={2023},
+      eprint={2306.02707},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+@misc{touvron2023llama,
+    title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
+    author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
+    year={2023},
+    eprint= arXiv 2307.09288
+}
+@misc{longpre2023flan,
+      title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, 
+      author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
+      year={2023},
+      eprint={2301.13688},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI}
+}
+@article{hu2021lora,
+  title={LoRA: Low-Rank Adaptation of Large Language Models},
+  author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
+  journal={CoRR},
+  year={2021}
+}
 ```