Upload README.md

README.md

@@ -1,13 +1,13 @@
 ---
-inference: false
-license: llama2
-pipeline_tag: text-generation
 datasets:
 - mlabonne/guanaco-llama2-1k
+inference: false
+license: llama2
 model_creator: MayaPH
 model_link: https://huggingface.co/MayaPH/GodziLLa2-70B
 model_name: GodziLLa2 70B
 model_type: llama
+pipeline_tag: text-generation
 quantized_by: TheBloke
 tags:
 - merge
@@ -16,17 +16,20 @@ 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>
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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 -->
 
 # GodziLLa2 70B - GGML
@@ -37,6 +40,14 @@ tags:
 
 This repo contains GGML format model files for [MayaPH's GodziLLa2 70B](https://huggingface.co/MayaPH/GodziLLa2-70B).
 
+### 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+The GGML format has now been superseded by GGUF.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.
@@ -48,7 +59,8 @@ GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NV
 ## Repositories available
 
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/GodziLLa2-70B-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/GodziLLa2-70B-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML)
 * [MayaPH's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/MayaPH/GodziLLa2-70B)
 
 ## Prompt template: Alpaca
@@ -60,12 +72,17 @@ Below is an instruction that describes a task. Write a response that appropriate
 {prompt}
 
 ### Response:
+
 ```
 
 <!-- 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.
 
@@ -94,57 +111,29 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [godzilla2-70b.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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. |
-| [godzilla2-70b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
-| [godzilla2-70b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
 | [godzilla2-70b.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
+| [godzilla2-70b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
+| [godzilla2-70b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
 | [godzilla2-70b.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. |
-| [godzilla2-70b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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. |
-| [godzilla2-70b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
 | [godzilla2-70b.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
+| [godzilla2-70b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
+| [godzilla2-70b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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. |
 | [godzilla2-70b.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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. |
-| [godzilla2-70b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
 | [godzilla2-70b.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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 |
-| godzilla2-70b.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. |
-| godzilla2-70b.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 |
-| godzilla2-70b.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. |
+| [godzilla2-70b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML/blob/main/godzilla2-70b.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.
 
-### q5_1, q6_K and q8_0 files require expansion from archive
+## How to run in `llama.cpp`
 
-**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.
+Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.
 
-<details>
-  <summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
-  
-### q5_1
-Please download:
-* `godzilla2-70b.ggmlv3.q5_1.zip`
-* `godzilla2-70b.ggmlv3.q5_1.z01`
- 
-### q6_K 
-Please download:
-* `godzilla2-70b.ggmlv3.q6_K.zip`
-* `godzilla2-70b.ggmlv3.q6_K.z01`
-
-### q8_0
-Please download:
-* `godzilla2-70b.ggmlv3.q8_0.zip`
-* `godzilla2-70b.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 godzilla2-70b.ggmlv3.q6_K.zip
-```
-</details>
-
-## How to run in `llama.cpp`
+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 godzilla2-70b.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a story about llamas\n\n### Response:"
+./main -t 10 -ngl 40 -gqa 8 -m godzilla2-70b.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\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`. If you are fully offloading the model to GPU, use `-t 1`
 
@@ -163,6 +152,7 @@ For other parameters and how to use them, please refer to [the llama.cpp documen
 Further instructions here: [text-generation-webui/docs/llama.cpp-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:
@@ -182,22 +172,25 @@ 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: MayaPH's GodziLLa2 70B
 
+
 <img src="https://drive.google.com/uc?export=view&id=1D8wxXkS1nsq3uqbOzOLwgx1cLJhY1nvN" alt="GodziLLa2-70B">
 Released August 11, 2023
 
 ## Model Description
-GodziLLa 2 70B is an experimental combination of various proprietary LoRAs from Maya Philippines and [Guanaco LLaMA 2 1K dataset](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k), with LLaMA 2 70B.  This model's primary purpose is to stress test the limitations of composite, instruction-following LLMs and observe its performance with respect to other LLMs available on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). This model debuted in the leaderboard at rank #4 (August 17, 2023).
+GodziLLa 2 70B is an experimental combination of various proprietary LoRAs from Maya Philippines and [Guanaco LLaMA 2 1K dataset](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k), with LLaMA 2 70B.  This model's primary purpose is to stress test the limitations of composite, instruction-following LLMs and observe its performance with respect to other LLMs available on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). This model debuted in the leaderboard at rank #4 (August 17, 2023) and operates under the Llama 2 license.
 ![Godzilla Happy GIF](https://i.pinimg.com/originals/81/3a/e0/813ae09a30f0bc44130cd2c834fe2eba.gif)
 
 ## Open LLM Leaderboard Metrics
@@ -215,8 +208,10 @@ According to the leaderboard description, here are the benchmarks used for the e
 - [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models.
 - [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - a test to measure a model’s propensity to reproduce falsehoods commonly found online.
 
+A detailed breakdown of the evaluation can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__GodziLLa2-70B). Huge thanks to [@thomwolf](https://huggingface.co/thomwolf).
+
 ## Leaderboard Highlights (as of August 17, 2023)
-- Godzilla 2 70B ranks 4th worldwide in the Open LLM Leaderboard.
+- Godzilla 2 70B debuts at 4th place worldwide in the Open LLM Leaderboard.
 - Godzilla 2 70B ranks #3 in the ARC challenge.
 - Godzilla 2 70B ranks #5 in the TruthfulQA benchmark.
 - *Godzilla 2 70B beats GPT-3.5 (ChatGPT) in terms of average performance and the HellaSwag benchmark (87.53 > 85.5).
@@ -274,6 +269,9 @@ python main.py --model hf-causal-experimental --model_args pretrained=MayaPH/God
 When using GodziLLa 2 70B, kindly take note of the following:
 - The default precision is `fp32`, and the total file size that would be loaded onto the RAM/VRAM is around 275 GB. Consider using a lower precision (fp16, int8, int4) to save memory.
 - To further save on memory, set the `low_cpu_mem_usage` argument to True.
+- If you wish to use a quantized version of GodziLLa2-70B, you can either access TheBloke's [GPTQ](https://huggingface.co/TheBloke/GodziLLa2-70B-GPTQ) or [GGML](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML) version of GodziLLa2-70B.
+  - [GodziLLa2-70B-GPTQ](https://huggingface.co/TheBloke/GodziLLa2-70B-GPTQ#description) is available in 4-bit and 3-bit
+  - [GodziLLa2-70B-GGML](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML#provided-files) is available in 8-bit, 6-bit, 5-bit, 4-bit, 3-bit, and 2-bit
 
 ## Ethical Considerations
 When using GodziLLa 2 70B, it is important to consider the following ethical considerations:
@@ -295,4 +293,4 @@ For additional information or inquiries about GodziLLa 2 70B, please contact the
 GodziLLa 2 70B is an AI language model from Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model.
 
 ## Acknowledgments
-The development of GodziLLa 2 70B was made possible by Maya Philippines and the curation of the various proprietary datasets and creation of the different proprietary LoRA adapters. Special thanks to mlabonne for the Guanaco dataset found [here](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k).
+The development of GodziLLa 2 70B was made possible by Maya Philippines and the curation of the various proprietary datasets and creation of the different proprietary LoRA adapters. Special thanks to mlabonne for the Guanaco dataset found [here](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k). Last but not least, huge thanks to [TheBloke](https://huggingface.co/TheBloke) for the quantized models, making our model easily accessible to a wider community.