Upload README.md

README.md

@@ -47,20 +47,17 @@ GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is
 
 The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.
 
-As of August 24th 2023, llama.cpp and KoboldCpp support GGUF. Other third-party clients and libraries are expected to add support very soon.
-
-Here is a list of clients and libraries that are known to support GGUF:
-* [llama.cpp](https://github.com/ggerganov/llama.cpp)
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp), now supports GGUF as of release 1.41!
-
-Here is a list of clients and libraries, along with their expected timeline for GGUF support. Where possible a link to the relevant issue or PR is provided:
-* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), awaiting llama-cpp-python support.
-* [LM Studio](https://lmstudio.ai/), in active development - hoped to be ready by August 25th-26th.
-* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), will work as soon as ctransformers or llama-cpp-python is updated.
-* [ctransformers](https://github.com/marella/ctransformers), [development will start soon](https://github.com/marella/ctransformers/issues/102).
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [in active development](https://github.com/abetlen/llama-cpp-python/issues/628).
-<!-- README_GGUF.md-about-gguf end -->
+Here are a list of clients and libraries that are known to support GGUF:
+* [llama.cpp](https://github.com/ggerganov/llama.cpp).
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
+* [LM Studio](https://lmstudio.ai/), version 0.2.2 and later support GGUF. A fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), should now work, choose the `c_transformers` backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
+* [ctransformers](https://github.com/marella/ctransformers), now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+* [candle](https://github.com/huggingface/candle), added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.
 
+<!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
 ## Repositories available
 
@@ -78,6 +75,7 @@ Here is a list of clients and libraries, along with their expected timeline for
 {prompt}
 
 ### RESPONSE:
+
 ```
 
 <!-- prompt-template end -->
@@ -86,9 +84,7 @@ Here is a list of clients and libraries, along with their expected timeline for
 
 These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9)
 
-As of August 24th 2023 they are now compatible with KoboldCpp, release 1.41 and later.
-
-They are are not yet compatible with any other third-party UIS, libraries or utilities but this is expected to change very soon.
+They are now also compatible with many third party UIs and libraries - please see the list at the top of the README.
 
 ## Explanation of quantisation methods
 <details>
@@ -110,16 +106,22 @@ 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 |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| [nous-puffin-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q2_K.gguf) | Q2_K | 2 | 29.11 GB| 31.61 GB | smallest, significant quality loss - not recommended for most purposes |
-| [nous-puffin-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.75 GB| 32.25 GB | very small, high quality loss |
-| [nous-puffin-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.10 GB| 35.60 GB | very small, high quality loss |
+| [nous-puffin-70b.Q6_K.gguf-split-b](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q6_K.gguf-split-b) | Q6_K | 6 | 19.89 GB| 22.39 GB | very large, extremely low quality loss |
+| [nous-puffin-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
+| [nous-puffin-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
+| [nous-puffin-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
 | [nous-puffin-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
-| [nous-puffin-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 38.99 GB| 41.49 GB | small, greater quality loss |
-| [nous-puffin-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.38 GB| 43.88 GB | medium, balanced quality - recommended |
+| [nous-puffin-70b.Q8_0.gguf-split-b](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q8_0.gguf-split-b) | Q8_0 | 8 | 36.59 GB| 39.09 GB | very large, extremely low quality loss - not recommended |
+| [nous-puffin-70b.Q6_K.gguf-split-a](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q6_K.gguf-split-a) | Q6_K | 6 | 36.70 GB| 39.20 GB | very large, extremely low quality loss |
+| [nous-puffin-70b.Q8_0.gguf-split-a](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q8_0.gguf-split-a) | Q8_0 | 8 | 36.70 GB| 39.20 GB | very large, extremely low quality loss - not recommended |
+| [nous-puffin-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [nous-puffin-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
+| [nous-puffin-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
+| [nous-puffin-70b.Q5_0.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
 | [nous-puffin-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
 | [nous-puffin-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF/blob/main/nous-puffin-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
-| nous-puffin-70b.Q6_K.gguf | q6_K | 6 | 56.82 GB | 59.32 GB | very large, extremely low quality loss |
-| nous-puffin-70b.Q8_0.gguf | q8_0 | 8 | 73.29 GB | 75.79 GB | very large, extremely low quality loss - not recommended |
+| nous-puffin-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
+| nous-puffin-70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 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.
 
@@ -157,25 +159,23 @@ del nous-puffin-70b.Q8_0.gguf-split-a nous-puffin-70b.Q8_0.gguf-split-b
 ```
 
 </details>
-
-
 <!-- README_GGUF.md-provided-files end -->
 
 <!-- README_GGUF.md-how-to-run start -->
-## How to run in `llama.cpp`
+## Example `llama.cpp` command
 
 Make sure you are using `llama.cpp` from commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9) or later.
 
-For compatibility with older versions of llama.cpp, or for use with third-party clients and libaries, please use GGML files instead.
+For compatibility with older versions of llama.cpp, or for any third-party libraries or clients that haven't yet updated for GGUF, please use GGML files instead.
 
 ```
-./main -t 10 -ngl 32 -m nous-puffin-70b.q4_K_M.gguf --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 nous-puffin-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### HUMAN:\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`.
+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 offloading all layers to GPU, set `-t 1`.
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 
-Change `-c 4096` to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters should be set by llama.cpp automatically.  If they are not, or if you need to change them manually, you can use `--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.
+Change `-c 4096` to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
 
 If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
 
@@ -184,6 +184,44 @@ 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.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
+
+## How to run from Python code
+
+You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
+
+### How to load this model from Python using ctransformers
+
+#### First install the package
+
+```bash
+# Base ctransformers with no GPU acceleration
+pip install ctransformers>=0.2.24
+# Or with CUDA GPU acceleration
+pip install ctransformers[cuda]>=0.2.24
+# Or with ROCm GPU acceleration
+CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
+# Or with Metal GPU acceleration for macOS systems
+CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
+```
+
+#### Simple example code to load one of these GGUF models
+
+```python
+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/Nous-Puffin-70B-GGUF", model_file="nous-puffin-70b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
+
+print(llm("AI is going to"))
+```
+
+## How to use with LangChain
+
+Here's guides on using llama-cpp-python or ctransformers with LangChain:
+
+* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
+* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
+
 <!-- README_GGUF.md-how-to-run end -->
 
 <!-- footer start -->
@@ -209,7 +247,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 
 **Special thanks to**: Aemon Algiz.
 
-**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
+**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!
@@ -236,7 +274,7 @@ Special thank you to Emozilla for assisting with training experimentations and b
 
 ## Model Training
 
-Redmond-Puffin 70B is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4. 
+Redmond-Puffin 70B is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4.
 
 Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math.
 
@@ -262,7 +300,7 @@ Optional reccomended pre-prompt / system prompt:
 Although full benchmarks have not completed for Puffin,
 Original Puffin 13B and Hermes-2 13B both beat previous SOTA for GPT4ALL benchmarks, with Hermes-2 winning by a 0.1% margin over Puffin.
 
-Overall, for general purpose zero-shot and/or single turn instructions, Hermes will likely be the way to go. Puffin may be prefferred for creative long conversation interactions, like having Puffin play a character or help brain storm creative ideas or concepts that make contextual sense within an already deep conversation. 
+Overall, for general purpose zero-shot and/or single turn instructions, Hermes will likely be the way to go. Puffin may be prefferred for creative long conversation interactions, like having Puffin play a character or help brain storm creative ideas or concepts that make contextual sense within an already deep conversation.
 
 Thank you to the comprehensive analysis and comparison of Puffin and Hermes by reddit user WolframRavenwolf here: https://www.reddit.com/r/LocalLLaMA/comments/158j9r9/nous_hermes_llama2_vs_redmond_puffin_13b/
 
@@ -270,13 +308,13 @@ Thank you to the comprehensive analysis and comparison of Puffin and Hermes by r
 
 ![puffin](https://i.imgur.com/P0MsN8B.png)
 
-![puffin](https://i.imgur.com/8EO3ThV.png) 
+![puffin](https://i.imgur.com/8EO3ThV.png)
 
-![puffin](https://i.imgur.com/5IWolFw.png) 
+![puffin](https://i.imgur.com/5IWolFw.png)
 
-![puffin](https://i.imgur.com/TQui8m7.png) 
+![puffin](https://i.imgur.com/TQui8m7.png)
 
-![puffin](https://i.imgur.com/tderIfl.png) 
+![puffin](https://i.imgur.com/tderIfl.png)
 
 ## Notable Features:
 
@@ -292,13 +330,13 @@ Thank you to the comprehensive analysis and comparison of Puffin and Hermes by r
 
 ## Future Plans
 
-This is a relatively early build amongst the grand plans for the future of Puffin! 
+This is a relatively early build amongst the grand plans for the future of Puffin!
 
 Current limitations: Some token mismatch problems have been identified, these may effect the current output quality, we plan to have this solved in Puffin V2 along with other improvements.
 
 ## How you can help!
 
-In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations. 
+In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations.
 
 If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
 
@@ -309,7 +347,7 @@ As of Puffins release, it achieves a new SOTA for the GPT4All benchmarks! Suppla
 
 Previous Sota: Hermes - 68.8
 New Sota:      Puffin - 69.9 (+1.1)
- 
+
 Puffin 13B supplants Hermes-2 for the #1 spot in Arc-E, HellaSwag and Winogrande!
 
 Puffin also perfectly ties with Hermes in PIQA, however Hermes-2 still excels in much of Big Bench and AGIEval, so it's highly reccomended you give it a try as well!