Update README.md

README.md

@@ -96,25 +96,6 @@ Refer to the Provided Files table below to see what files use which methods, and
 <!-- compatibility_gguf end -->
 
 <!-- README_GGUF.md-provided-files start -->
-## Provided files
-
-| Name | Quant method | Bits | Size | Max RAM required | Use case |
-| ---- | ---- | ---- | ---- | ---- | ----- |
-| [codellama-13b-instruct.Q2_K.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
-| [codellama-13b-instruct.Q3_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
-| [codellama-13b-instruct.Q3_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
-| [codellama-13b-instruct.Q3_K_L.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
-| [codellama-13b-instruct.Q4_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [codellama-13b-instruct.Q4_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
-| [codellama-13b-instruct.Q4_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
-| [codellama-13b-instruct.Q5_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [codellama-13b-instruct.Q5_K_S.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
-| [codellama-13b-instruct.Q5_K_M.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
-| [codellama-13b-instruct.Q6_K.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
-| [codellama-13b-instruct.Q8_0.gguf](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/blob/main/codellama-13b-instruct.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
-
-**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.
-
 
 
 <!-- README_GGUF.md-provided-files end -->
@@ -146,7 +127,7 @@ pip3 install huggingface-hub>=0.17.1
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 
 ```shell
-huggingface-cli download TheBloke/CodeLlama-13B-Instruct-GGUF codellama-13b-instruct.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download DevShubham/Codellama-7B-Instruct-GGUF codellama-13b-instruct.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -155,7 +136,7 @@ huggingface-cli download TheBloke/CodeLlama-13B-Instruct-GGUF codellama-13b-inst
 You can also download multiple files at once with a pattern:
 
 ```shell
-huggingface-cli download TheBloke/CodeLlama-13B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
+huggingface-cli download DevShubham/Codellama-7B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
 ```
 
 For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
@@ -222,7 +203,7 @@ CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
 from ctransformers import AutoModelForCausalLM
 
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-13B-Instruct-GGUF", model_file="codellama-13b-instruct.q4_K_M.gguf", model_type="llama", gpu_layers=50)
+llm = AutoModelForCausalLM.from_pretrained("DevShubham/Codellama-7B-Instruct-GGUF", model_file="codellama-13b-instruct.q4_K_M.gguf", model_type="llama", gpu_layers=50)
 
 print(llm("AI is going to"))
 ```
@@ -237,14 +218,7 @@ Here's guides on using llama-cpp-python or ctransformers with LangChain:
 <!-- README_GGUF.md-how-to-run end -->
 
 <!-- footer start -->
-<!-- 200823 -->
-## Discord
-
-For further support, and discussions on these models and AI in general, join us at:
 
-[TheBloke AI's Discord server](https://discord.gg/theblokeai)
-
-## Thanks, and how to contribute
 
 Thanks to the [chirper.ai](https://chirper.ai) team!
 
@@ -256,14 +230,6 @@ If you're able and willing to contribute it will be most gratefully received and
 
 Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
 
-* Patreon: https://patreon.com/TheBlokeAI
-* Ko-Fi: https://ko-fi.com/TheBlokeAI
-
-**Special thanks to**: Aemon Algiz.
-
-**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
-
-
 Thank you to all my generous patrons and donaters!
 
 And thank you again to a16z for their generous grant.