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
 
@@ -79,6 +76,7 @@ Here is a list of clients and libraries, along with their expected timeline for
 {prompt}
 
 ### Response:
+
 ```
 
 <!-- prompt-template end -->
@@ -87,9 +85,7 @@ 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>
@@ -111,16 +107,22 @@ 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-hermes-llama2-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q2_K.gguf) | Q2_K | 2 | 29.48 GB| 31.98 GB | smallest, significant quality loss - not recommended for most purposes |
-| [nous-hermes-llama2-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 30.09 GB| 32.59 GB | very small, high quality loss |
-| [nous-hermes-llama2-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.45 GB| 35.95 GB | very small, high quality loss |
-| [nous-hermes-llama2-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.49 GB| 38.99 GB | small, substantial quality loss |
-| [nous-hermes-llama2-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.30 GB| 41.80 GB | small, greater quality loss |
-| [nous-hermes-llama2-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.69 GB| 44.19 GB | medium, balanced quality - recommended |
-| [nous-hermes-llama2-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.74 GB| 50.24 GB | large, low quality loss - recommended |
-| [nous-hermes-llama2-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 49.03 GB| 51.53 GB | large, very low quality loss - recommended |
-| nous-hermes-llama2-70b.Q6_K.bin | q6_K | 6 | 56.82 GB | 59.32 GB | very large, extremely low quality loss |
-| nous-hermes-llama2-70b.Q8_0.bin | q8_0 | 8 | 73.29 GB | 75.79 GB | very large, extremely low quality loss - not recommended |
+| [nous-hermes-llama2-70b.Q6_K.gguf-split-b](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q6_K.gguf-split-b) | Q6_K | 6 | 19.89 GB| 22.39 GB | very large, extremely low quality loss |
+| [nous-hermes-llama2-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
+| [nous-hermes-llama2-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
+| [nous-hermes-llama2-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
+| [nous-hermes-llama2-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
+| [nous-hermes-llama2-70b.Q8_0.gguf-split-b](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q8_0.gguf-split-b) | Q8_0 | 8 | 36.59 GB| 39.09 GB | very large, extremely low quality loss - not recommended |
+| [nous-hermes-llama2-70b.Q6_K.gguf-split-a](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q6_K.gguf-split-a) | Q6_K | 6 | 36.70 GB| 39.20 GB | very large, extremely low quality loss |
+| [nous-hermes-llama2-70b.Q8_0.gguf-split-a](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q8_0.gguf-split-a) | Q8_0 | 8 | 36.70 GB| 39.20 GB | very large, extremely low quality loss - not recommended |
+| [nous-hermes-llama2-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [nous-hermes-llama2-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
+| [nous-hermes-llama2-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
+| [nous-hermes-llama2-70b.Q5_0.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [nous-hermes-llama2-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
+| [nous-hermes-llama2-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-70B-GGUF/blob/main/nous-hermes-llama2-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
+| nous-hermes-llama2-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
+| nous-hermes-llama2-70b.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.
 
@@ -143,7 +145,7 @@ Please download:
 
 To join the files, do the following:
 
-Linux:
+Linux and macOS:
 ```
 cat nous-hermes-llama2-70b.Q6_K.gguf-split-* > nous-hermes-llama2-70b.Q6_K.gguf && rm nous-hermes-llama2-70b.Q6_K.gguf-split-*
 cat nous-hermes-llama2-70b.Q8_0.gguf-split-* > nous-hermes-llama2-70b.Q8_0.gguf && rm nous-hermes-llama2-70b.Q8_0.gguf-split-*
@@ -158,9 +160,71 @@ del nous-hermes-llama2-70b.Q8_0.gguf-split-a nous-hermes-llama2-70b.Q8_0.gguf-sp
 ```
 
 </details>
-
 <!-- README_GGUF.md-provided-files end -->
 
+<!-- README_GGUF.md-how-to-run start -->
+## 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 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-hermes-llama2-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction:\n\nWrite a story about llamas\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 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 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`
+
+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.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-Hermes-Llama2-70B-GGUF", model_file="nous-hermes-llama2-70b.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 -->
 <!-- 200823 -->
 ## Discord
@@ -184,7 +248,7 @@ 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!
@@ -216,16 +280,16 @@ The model was trained almost entirely on synthetic GPT-4 outputs. Curating high
 This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
 
 ## Collaborators
-The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Pygmalion AI. 
-  
+The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Pygmalion AI.
+
 Special mention goes to @winglian for assisting in some of the training issues.
 
-Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. 
+Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.
 
 Among the contributors of datasets:
 - GPTeacher was made available by Teknium
 - Wizard LM by nlpxucan
-- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.  
+- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
 - GPT4-LLM and Unnatural Instructions were provided by Microsoft
 - Airoboros dataset by jondurbin
 - Camel-AI's domain expert datasets are from Camel-AI
@@ -245,7 +309,7 @@ The model follows the Alpaca prompt format:
 
 ```
 
-or 
+or
 
 ```
 ### Instruction:
@@ -261,7 +325,7 @@ or
 
 ## Benchmarks:
 
-GPT4All Suite:  
+GPT4All Suite:
 
 ```
 hf-causal-experimental (pretrained=/home/data/axolotl/Nous-Hermes-Llama2-70b,dtype=float16,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
@@ -306,7 +370,7 @@ hf-causal-experimental (pretrained=/home/data/axolotl/Nous-Hermes-Llama2-70b,dty
 |bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2168|±  |0.0117|
 |bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1531|±  |0.0086|
 |bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4467|±  |0.0288|
-```  
+```
 
 AGIEval:
 ```
@@ -329,15 +393,15 @@ hf-causal-experimental (pretrained=/home/data/axolotl/Nous-Hermes-Llama2-70b,dty
 |                              |       |acc_norm|0.4709|±  |0.0349|
 |agieval_sat_math              |      0|acc     |0.4136|±  |0.0333|
 |                              |       |acc_norm|0.3455|±  |0.0321|
-```  
+```
 
 ## Resources for Applied Use Cases:
 Check out LM Studio for a nice chatgpt style interface here: https://lmstudio.ai/
-For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord  
-For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot  
+For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord
+For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot
 
 ## Future Plans
-We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. 
+We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.
 
 ## Model Usage
 The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.