Upload README.md

README.md

@@ -2,7 +2,7 @@
 inference: false
 language:
 - en
-license: other
+license: llama2
 model_creator: Upstage
 model_link: https://huggingface.co/upstage/Llama-2-70b-instruct-v2
 model_name: Llama 2 70B Instruct v2
@@ -17,17 +17,20 @@ tags:
 ---
 
 <!-- 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 -->
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+<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 -->
 
 # Llama 2 70B Instruct v2 - GGML
@@ -38,36 +41,50 @@ tags:
 
 This repo contains GGML format model files for [Upstage's Llama 2 70B Instruct v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2).
 
-CUDA GPU acceleration is now available for Llama 2 70B GGML files. Metal acceleration (macOS) is not yet available. I haven't tested AMD acceleration - let me know if it works. The following clients/libraries are known to work with these files, including with CUDA GPU acceleration:
+### 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+The GGML format has now been superseded by GGUF.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
+
+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:
 * [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
 * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI.
 * [KoboldCpp](https://github.com/LostRuins/koboldcpp), version 1.37 and later. A powerful GGML web UI, especially good for story telling.
-* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration. (GPU acceleration is Windows only for 70B models at the moment.)
+* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration for both Windows and macOS. Use 0.1.11 or later for macOS GPU acceleration with 70B models.
 * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.
 * [ctransformers](https://github.com/marella/ctransformers), version 0.2.15 and later. A Python library with LangChain support, and OpenAI-compatible API server.
 
 ## Repositories available
 
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML)
 * [Upstage's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)
 
 ## Prompt template: Orca-Hashes
 
 ```
 ### System:
-This is a system prompt, please behave and help the user.
+{system_message}
 
 ### User:
 {prompt}
 
 ### Assistant:
+
 ```
 
 <!-- compatibility_ggml start -->
 ## Compatibility
 
-### Requires llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) or later.
+### Works with llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) until August 21st, 2023
+
+Will not work with `llama.cpp` after commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa).
+
+For compatibility with latest llama.cpp, please use GGUF files instead.
 
 Or one of the other tools and libraries listed above.
 
@@ -96,70 +113,48 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q2_K.bin) | q2_K | 2 | 28.59 GB| 31.09 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. |
-| [upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 36.15 GB| 38.65 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 |
-| [upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 33.04 GB| 35.54 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 |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 29.75 GB| 32.25 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| [upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 33.04 GB| 35.54 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 |
+| [upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 36.15 GB| 38.65 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 |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. |
-| [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_1.bin) | q4_1 | 4 | 43.17 GB| 45.67 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. |
-| [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 41.38 GB| 43.88 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 |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 38.87 GB| 41.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 41.38 GB| 43.88 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 |
+| [upstage-llama-2-70b-instruct-v2.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.ggmlv3.q4_1.bin) | q4_1 | 4 | 43.17 GB| 45.67 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. |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.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. |
-| [upstage-llama-2-70b-instruct-v2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.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 |
 | [upstage-llama-2-70b-instruct-v2.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.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 |
-| upstage-llama-2-70b-instruct-v2.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. |
-| upstage-llama-2-70b-instruct-v2.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 |
-| upstage-llama-2-70b-instruct-v2.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. |
-
-### q5_1, q6_K and q8_0 files require expansion from archive
-
-**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the q6_K and q8_0 files as multi-part ZIP files. They are not compressed, they are just for storing a .bin file in two parts.
-
-<details>
-  <summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
-  
-### q5_1
-Please download:
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q5_1.zip`
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q5_1.z01`
- 
-### q6_K 
-Please download:
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q6_K.zip`
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q6_K.z01`
-
-### q8_0
-Please download:
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q8_0.zip`
-* `upstage-llama-2-70b-instruct-v2.ggmlv3.q8_0.z01`
-
-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:
-```
-sudo apt update -y && sudo apt install 7zip
-7zz x upstage-llama-2-70b-instruct-v2.ggmlv3.q6_K.zip
-</details>
+| [upstage-llama-2-70b-instruct-v2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGML/blob/main/upstage-llama-2-70b-instruct-v2.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 |
 
 **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.
 
 ## How to run in `llama.cpp`
 
+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.
+
 I use the following command line; adjust for your tastes and needs:
 
 ```
-./main -t 10 -ngl 40 -gqa 8 -m upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nThis is a system prompt, please behave and help the user.\n\n### User:\nWrite a story about llamas\n\n### Assistant:"
+./main -t 10 -ngl 40 -gqa 8 -m upstage-llama-2-70b-instruct-v2.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n\n### User:\n{prompt}\n\n### 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`.
+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`
 
-Change -ngl 40 to the number of GPU layers you have VRAM for. Use -ngl 100 to offload all layers to VRAM, if you have a 48GB card, or 2 x 24GB, or similar.  Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.
+Change `-ngl 40` to the number of GPU layers you have VRAM for. Use `-ngl 100` to offload all layers to VRAM - if you have a 48GB card, or 2 x 24GB, or similar.  Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.
+
+If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
 
 Remember the `-gqa 8` argument, required for Llama 70B models.
 
-If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
+Change `-c 4096` to the desired sequence length for this model. For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context.
+
+For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
 
 ## 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).
 
 <!-- footer start -->
+<!-- 200823 -->
 ## Discord
 
 For further support, and discussions on these models and AI in general, join us at:
@@ -179,18 +174,21 @@ 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**: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse
+**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: Upstage's Llama 2 70B Instruct v2
 
-# LLaMa-2-70b-instruct-v2 model card
+# SOLAR-0-70b-16bit model card
+The model name has been changed from LLaMa-2-70b-instruct-v2 to SOLAR-0-70b-16bit
 
 ## Model Details
 
@@ -206,69 +204,76 @@ Thank you to all my generous patrons and donaters!
 
 ### Used Datasets
 - Orca-style dataset
-- Alpaca-Style Dataset
+- Alpaca-style dataset
+- No other dataset was used except for the dataset mentioned above
+- No benchmark test set or the training set are used
 
 
 ### Prompt Template
 ```
 ### System:
 {System}
+
 ### User:
 {User}
+
 ### Assistant:
 {Assistant}
 ```
-### Usage
 
-*Tested on A100 80GB*
+## Usage
+
+- The followings are tested on A100 80GB
+- Our model can handle up to 10k+ input tokens, thanks to the `rope_scaling` option
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
 tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
 model = AutoModelForCausalLM.from_pretrained(
     "upstage/Llama-2-70b-instruct-v2",
-    device_map='auto',
+    device_map="auto",
     torch_dtype=torch.float16,
     load_in_8bit=True,
-    rope_scaling={'type': 'dynamic', 'factor': 2} # longer inputs possible
+    rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
 )
-prompt = "### User:\nThomas is very healthy, but he has to go to the hospital every day. What could be the reasons?\n\n### Assistant:\n"
+
+prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-del inputs['token_type_ids']
+del inputs["token_type_ids"]
 streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
 output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
 output_text = tokenizer.decode(output[0], skip_special_tokens=True)
 ```
 
-**Our model can handle >10k input tokens thanks to the `rope_scaling` option.**
-
 ## Hardware and Software
 
 * **Hardware**: We utilized an A100x8 * 4 for training our model
-* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)
+* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)
 
 ## Evaluation Results
 
 ### Overview
-- We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
-We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`.
+- We conducted a performance evaluation following the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`
 We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463).
+- We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models
 
 ### Main Results
-| Model                                         | H4 Average | ARC   | HellaSwag | MMLU  | TruthfulQA |       | MT_Bench |
-|-----------------------------------------------|---------|-------|-----------|-------|------------|-------|----------|
-| **Llama-2-70b-instruct-v2** (***Ours***, ***Local Reproduction***) | **72.7** | **71.6** | **87.7** | **69.7** | **61.6** |       | 7.440625 |
-| Llama-2-70b-instruct (Ours, Local Reproduction) | 72.0 | 70.7 | 87.4 | 69.3 | 60.7 |       | 7.24375  |
-| llama-65b-instruct (Ours, Local Reproduction) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 |       |          |
-| Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 |       |          |
-| llama-30b-instruct-2048 (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 |       | |
-| llama-30b-instruct-2048 (Ours, Local Reproduction) | 67.0 | 64.9 | 85.0 | 61.9 | 56.0 |       | 6.88125 |
-| llama-30b-instruct (Ours, Open LLM Leaderboard) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 |       |          |
-| llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 |       |          |
-| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 |       |          |
-
-### Scripts
+| Model | H4(Avg) | ARC | HellaSwag | MMLU | TruthfulQA | | MT_Bench |
+|--------------------------------------------------------------------|----------|----------|----------|------|----------|-|-------------|
+| **[Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)**(***Ours***, ***Open LLM Leaderboard***) | **73** | **71.1** | **87.9** | **70.6** | **62.2** | | **7.44063** |
+| [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) | 72.3 | 70.9 | 87.5 | 69.8 | 61 | | 7.24375  |
+| [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | | |
+| Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | | |
+| [llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048) (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | | |
+| [llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) (Ours, Open LLM Leaderboard) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | | |
+| llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 | | |
+| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | | |
+
+### Scripts for H4 Score Reproduction
 - Prepare evaluation environments:
 ```
 # clone the repository
@@ -279,12 +284,9 @@ git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
 cd lm-evaluation-harness
 ```
 
-## Ethical Issues
-
-### Ethical Considerations
-- There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process.
-
 ## Contact Us
 
-### Why Upstage LLM?
-- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model **outperforms all models around the world**,  positioning itself as the leading performer. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm).
+### About Upstage
+- [Upstage](https://en.upstage.ai) is a company specialized in Large Language Models (LLMs) and AI. We will help you build private LLMs and related applications.
+If you have a dataset to build domain specific LLMs or make LLM applications, please contact us at ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm)
+- As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally.