--- datasets: - LDJnr/Puffin inference: false language: - eng license: llama2 model_creator: NousResearch model_link: https://huggingface.co/NousResearch/Nous-puffin-70b model_name: Nous Puffin 70B model_type: llama quantized_by: TheBloke tags: - llama-2 - sft ---
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# Nous Puffin 70B - GGUF - Model creator: [NousResearch](https://huggingface.co/NousResearch) - Original model: [Nous Puffin 70B](https://huggingface.co/NousResearch/Nous-puffin-70b) ## Description This repo contains GGUF format model files for [NousResearch's Nous Puffin 70B](https://huggingface.co/NousResearch/Nous-puffin-70b). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. 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. 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, with many features and powerful extensions. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with full GPU accel across multiple platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Puffin-70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGUF) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Nous-Puffin-70B-GGML) * [NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-puffin-70b) ## Prompt template: Human-Response ``` ### HUMAN: {prompt} ### RESPONSE: ``` ## Compatibility 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) 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
Click to see details The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how.
## Provided files | 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 | 29.28 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_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 | 56.59 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. ### Q6_K and Q8_0 files are split and require joining **Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
Click for instructions regarding Q6_K and Q8_0 files ### q6_K Please download: * `nous-puffin-70b.Q6_K.gguf-split-a` * `nous-puffin-70b.Q6_K.gguf-split-b` ### q8_0 Please download: * `nous-puffin-70b.Q8_0.gguf-split-a` * `nous-puffin-70b.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat nous-puffin-70b.Q6_K.gguf-split-* > nous-puffin-70b.Q6_K.gguf && rm nous-puffin-70b.Q6_K.gguf-split-* cat nous-puffin-70b.Q8_0.gguf-split-* > nous-puffin-70b.Q8_0.gguf && rm nous-puffin-70b.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B nous-puffin-70b.Q6_K.gguf-split-a + nous-puffin-70b.Q6_K.gguf-split-b nous-puffin-70b.Q6_K.gguf del nous-puffin-70b.Q6_K.gguf-split-a nous-puffin-70b.Q6_K.gguf-split-b COPY /B nous-puffin-70b.Q8_0.gguf-split-a + nous-puffin-70b.Q8_0.gguf-split-b nous-puffin-70b.Q8_0.gguf del nous-puffin-70b.Q8_0.gguf-split-a nous-puffin-70b.Q8_0.gguf-split-b ```
## 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-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`. 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 ` 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-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) ## 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! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. 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**: 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. # Original model card: NousResearch's Nous Puffin 70B ## **Redmond-Puffin-70B** **Based off Puffin 13B which was the first commercially available language model released by Nous Research!** Compute provided by PygmalionAI, thank you! Follow PygmalionAI on Twitter @pygmalion_ai. This is a larger version of Puffin which was originally the worlds first third-party llama-2 fine-tune. leveraging a hand curated set of 3K high quality examples, many of which take full advantage of the 4096 context length of Llama 2. This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha. Special thank you to Pygmalion AI for sponsoring the compute. Special thank you to Emozilla for assisting with training experimentations and benchmarking. ## 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. Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math. ## Prompt Format The reccomended model usage is: ``` ### human: ### response: ``` Optional reccomended pre-prompt / system prompt: ``` ### human: Interact in conversation to the best of your ability, please be concise, logical, intelligent and coherent. ### response: Sure! sounds good. ``` ## When should I use Puffin or Hermes 2? 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. 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/ ## Example Outputs!: ![puffin](https://i.imgur.com/P0MsN8B.png) ![puffin](https://i.imgur.com/8EO3ThV.png) ![puffin](https://i.imgur.com/5IWolFw.png) ![puffin](https://i.imgur.com/TQui8m7.png) ![puffin](https://i.imgur.com/tderIfl.png) ## Notable Features: - The first Llama-2 based fine-tuned model released by Nous Research. - Ability to recall information upto 2023 without internet (ChatGPT cut off date is in 2021) - Pretrained on 2 trillion tokens of text. (This is double the amount of most Open LLM's) - Pretrained with a context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit. - The first commercially available language model released by Nous Research. ## Future Plans 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. 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! ## Benchmarks (New benchmarks coming soon, however here are the 13B benchmarks for now)! As of Puffins release, it achieves a new SOTA for the GPT4All benchmarks! Supplanting Hermes for the #1 position! (Rounded to nearest tenth) 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!