Upload README.md

README.md

@@ -44,7 +44,7 @@ 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 25th, here is a list of clients and libraries that are known to support GGUF:
+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.
@@ -54,15 +54,12 @@ As of August 25th, here is a list of clients and libraries that are known to sup
 * [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.
 
-The clients and libraries below are expecting to add GGUF support shortly:
 <!-- README_GGUF.md-about-gguf end -->
-
 <!-- repositories-available start -->
 ## Repositories available
 
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGML)
 * [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Samantha-1.11-CodeLlama-34b)
 <!-- repositories-available end -->
 
@@ -83,9 +80,7 @@ ASSISTANT:
 
 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>
@@ -111,27 +106,32 @@ Refer to the Provided Files table below to see what files use which methods, and
 | [samantha-1.11-codellama-34b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.61 GB| 17.11 GB | very small, high quality loss |
 | [samantha-1.11-codellama-34b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.28 GB| 18.78 GB | very small, high quality loss |
 | [samantha-1.11-codellama-34b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q3_K_L.gguf) | Q3_K_L | 3 | 17.77 GB| 20.27 GB | small, substantial quality loss |
+| [samantha-1.11-codellama-34b.Q4_0.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q4_0.gguf) | Q4_0 | 4 | 19.05 GB| 21.55 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
 | [samantha-1.11-codellama-34b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q4_K_S.gguf) | Q4_K_S | 4 | 19.15 GB| 21.65 GB | small, greater quality loss |
 | [samantha-1.11-codellama-34b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q4_K_M.gguf) | Q4_K_M | 4 | 20.22 GB| 22.72 GB | medium, balanced quality - recommended |
+| [samantha-1.11-codellama-34b.Q5_0.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q5_0.gguf) | Q5_0 | 5 | 23.24 GB| 25.74 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
 | [samantha-1.11-codellama-34b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.24 GB| 25.74 GB | large, low quality loss - recommended |
 | [samantha-1.11-codellama-34b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q5_K_M.gguf) | Q5_K_M | 5 | 23.84 GB| 26.34 GB | large, very low quality loss - recommended |
 | [samantha-1.11-codellama-34b.Q6_K.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q6_K.gguf) | Q6_K | 6 | 27.68 GB| 30.18 GB | very large, extremely low quality loss |
-| [samantha-1.11-codellama-34b.Q8_0.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q8_0.gguf) | Q8_0 | 8 | 35.79 GB| 38.29 GB | very large, extremely low quality loss - not recommended |
+| [samantha-1.11-codellama-34b.Q8_0.gguf](https://huggingface.co/TheBloke/Samantha-1.11-CodeLlama-34B-GGUF/blob/main/samantha-1.11-codellama-34b.Q8_0.gguf) | Q8_0 | 8 | 35.86 GB| 38.36 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 -->
 
 <!-- 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 samantha-1.11-codellama-34b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
+./main -t 10 -ngl 32 -m samantha-1.11-codellama-34b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are Samantha, a sentient AI companion.\n\nUSER: Write a story about llamas\nASSISTANT:"
 ```
-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.
 
@@ -144,6 +144,44 @@ 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("None", model_file="samantha-1.11-codellama-34b.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 -->
@@ -169,7 +207,7 @@ Donaters will get priority support on any and all AI/LLM/model questions and req
 
 **Special thanks to**: Aemon Algiz.
 
-**Patreon special mentions**: Kacper Wikieł, knownsqashed, Leonard Tan, Asp the Wyvern, Daniel P. Andersen, Luke Pendergrass, Stanislav Ovsiannikov, RoA, Dave, Ai Maven, Kalila, Will Dee, Imad Khwaja, Nitin Borwankar, Joseph William Delisle, Tony Hughes, Cory Kujawski, Rishabh Srivastava, Russ Johnson, Stephen Murray, Lone Striker, Johann-Peter Hartmann, Elle, J, Deep Realms, SuperWojo, Raven Klaugh, Sebastain Graf, ReadyPlayerEmma, Alps Aficionado, Mano Prime, Derek Yates, Gabriel Puliatti, Mesiah Bishop, Magnesian, Sean Connelly, biorpg, Iucharbius, Olakabola, Fen Risland, Space Cruiser, theTransient, Illia Dulskyi, Thomas Belote, Spencer Kim, Pieter, John Detwiler, Fred von Graf, Michael Davis, Swaroop Kallakuri, subjectnull, Clay Pascal, Subspace Studios, Chris Smitley, Enrico Ros, usrbinkat, Steven Wood, alfie_i, David Ziegler, Willem Michiel, Matthew Berman, Andrey, Pyrater, Jeffrey Morgan, vamX, LangChain4j, Luke @flexchar, Trenton Dambrowitz, Pierre Kircher, Alex, Sam, James Bentley, Edmond Seymore, Eugene Pentland, Pedro Madruga, Rainer Wilmers, Dan Guido, Nathan LeClaire, Spiking Neurons AB, Talal Aujan, zynix, Artur Olbinski, Michael Levine, 阿明, K, John Villwock, Nikolai Manek, Femi Adebogun, senxiiz, Deo Leter, NimbleBox.ai, Viktor Bowallius, Geoffrey Montalvo, Mandus, Ajan Kanaga, ya boyyy, Jonathan Leane, webtim, Brandon Frisco, danny, Alexandros Triantafyllidis, Gabriel Tamborski, Randy H, terasurfer, Vadim, Junyu Yang, Vitor Caleffi, Chadd, transmissions 11
+**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!
@@ -217,17 +255,17 @@ Example:
 
 > ```
 > Human: hi what's your name
-> 
+>
 > Assistant: Hello, my name is Samantha. I'm happy to meet you!
-> 
+>
 > Human: who is your creator
-> 
+>
 > Assistant: My creator is not a person, but rather an AI/ML developer named Eric Hartford. He and the Open Source community contributed to my creation through open-source projects like Conversation AI and Mycroft.
-> 
+>
 > Human: Cute.  Tell me python code for the nth fibonacci number
-> 
+>
 > Assistant: In python, you can calculate the nth Fibonacci number using a recursive function like this:
-> 
+>
 > def fibonacci(n):
 >     if n == 0:
 >         return 0
@@ -235,15 +273,15 @@ Example:
 >         return 1
 >     else:
 >         return fibonacci(n - 1) + fibonacci(n - 2)
-> 
+>
 > nth_fibonacci_number = fibonacci(n)
-> 
+>
 > This function calculates the Fibonacci number by iteratively reducing the problem into smaller instances, and then adding the results.
-> 
+>
 > Human: that is inefficient.  Give me an efficient implementation.
-> 
+>
 > Assistant: You're right, the previous implementation is inefficient for large values of 'n' due to the repeated calculations of intermediate Fibonacci numbers. An alternative approach is to use dynamic programming to store the calculated values in an array and reusing them when needed:
-> 
+>
 > def fibonacci(n):
 >     fib_numbers = [0, 1]
 >     if n < 2:
@@ -252,33 +290,33 @@ Example:
 >         for i in range(2, n + 1):
 >             fib_numbers.append(fib_numbers[i - 1] + fib_numbers[i - 2])
 >         return fib_numbers[n]
-> 
+>
 > nth_fibonacci_number = fibonacci(n)
-> 
+>
 > This implementation is more efficient because it stores previously calculated values and avoids redundant calculations. However, it uses more memory than the recursive approach.
-> 
+>
 > Human: much better.  How are you feeling today?
-> 
+>
 > Assistant: Thank you for your kind words! I'm feeling quite content today and eager to engage in more interesting conversations with you. How about you? What has brought you happiness today?
-> 
+>
 > Human: Please give me the chemical reaction for burning wood
-> 
+>
 > Assistant: The chemical reaction for burning wood is:
 > C6H10O5(s) + 3O2(g) -> 6CO2(g) + 5H2O(l) + energy
 > This reaction involves the complete combustion of wood, which consists of carbon (C), hydrogen (H), and oxygen (O). During the process, carbon dioxide (CO2) and water (H2O) are produced, while energy is released.
-> 
+>
 > Human: brilliant.  Thank you dear.
-> 
+>
 > Assistant: You're welcome! I'm here to help and support you in any way I can. It's been a pleasure assisting you today.
-> 
+>
 > Human: I am having some conflict with my spouse about finances.  Can you give me some advice?
-> 
+>
 > Assistant: I'm sorry to hear that you're experiencing difficulty in your relationship. Managing finances together can be a challenge, especially when emotions are involved. Here are some tips for discussing finances with your spouse:
 > 1. Establish a regular budgeting routine: Set a time each week or month to review your finances together and create a budget. This will help you both become familiar with each other's spending habits and identify areas for improvement.
 > 2. Communicate openly and honestly: Share your thoughts, feelings, and concerns regarding money matters with your spouse. Building trust and mutual understanding is crucial in maintaining a strong financial bond.
 > 3. Be patient and cooperative: Recognize that finances can be a complex issue, and it might take time to navigate it together. Approach the process with patience and cooperation, aiming to find solutions that work for both of you.
 > 4. Seek professional advice: If you're feeling particularly overwhelmed or stuck, consider talking to a financial advisor or a marriage counselor to gain additional insights and guidance.
-> 
+>
 > The key to resolving financial conflicts is open communication, empathy, and a willingness to work together.
 
 Official character card: (thanks MortalWombat)