metadata

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

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Nous Puffin 70B - GGUF

Model creator: NousResearch
Original model: Nous Puffin 70B

Description

This repo contains GGUF format model files for NousResearch's 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.

As of August 23rd 2023, only llama.cpp supports GGUF. However, third-party clients and libraries are expected to add support very soon.

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, awaiting llama-cpp-python support.
KoboldCpp, in active development. Test builds are working, but GPU acceleration remains to be tested.
LM Studio, in active development - hoped to be ready by August 25th-26th.
LoLLMS Web UI, will work as soon as ctransformers or llama-cpp-python is updated.
ctransformers, development will start soon.
llama-cpp-python, in active development.

Repositories available

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

As of August 23rd 2023 they are not yet compatible with any third-party UIS, libraries or utilities but this is expected to change very soon.

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
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

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	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	Q3_K_S	3	29.75 GB	32.25 GB	very small, high quality loss
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.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	Q4_K_S	4	38.99 GB	41.49 GB	small, greater quality loss
nous-puffin-70b.Q4_K_M.gguf	Q4_K_M	4	41.38 GB	43.88 GB	medium, balanced quality - recommended
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	Q5_K_M	5	48.75 GB	51.25 GB	large, very low quality loss - recommended
nous-puffin-70b.Q6_K.bin	q6_K	6	56.82 GB	59.32 GB	very large, extremely low quality loss
nous-puffin-70b.Q8_0.bin	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

How to run in `llama.cpp`

Make sure you are using llama.cpp from 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.

./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:"

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 -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.

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

How to run in `text-generation-webui`

Further instructions here: text-generation-webui/docs/llama.cpp.md.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the 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: 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

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!:

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!