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metadata
language:
  - eng
license:
  - mit
tags:
  - llama-2
  - sft
datasets:
  - LDJnr/Puffin
model_name: Redmond Puffin 13B V1.3
base_model: NousResearch/Redmond-Puffin-13B
inference: false
model_creator: NousResearch
model_type: llama
prompt_template: |
  ### human: {prompt}

  ### response:
quantized_by: TheBloke
TheBlokeAI

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

Redmond Puffin 13B V1.3 - GPTQ

Description

This repo contains GPTQ model files for NousResearch's Redmond Puffin 13B V1.3.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

Repositories available

Prompt template: Human-Response2 

### human: {prompt}

### response:

Licensing

The creator of the source model has listed its license as ['mit'], and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: NousResearch's Redmond Puffin 13B V1.3.

Provided files and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the main branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.

Explanation of GPTQ parameters
  • Bits: The bit size of the quantised model.
  • GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
  • Act Order: True or False. Also known as desc_act. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
  • Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
  • GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
  • Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
  • ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
Branch Bits GS Act Order Damp % GPTQ Dataset Seq Len Size ExLlama Desc
main 4 128 No 0.01 wikitext 4096 7.26 GB Yes 4-bit, without Act Order and group size 128g.
gptq-4bit-32g-actorder_True 4 32 Yes 0.01 wikitext 4096 8.00 GB Yes 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-4bit-64g-actorder_True 4 64 Yes 0.01 wikitext 4096 7.51 GB Yes 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.
gptq-4bit-128g-actorder_True 4 128 Yes 0.01 wikitext 4096 7.26 GB Yes 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy.
gptq-8bit--1g-actorder_True 8 None Yes 0.01 wikitext 4096 13.36 GB No 8-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_False 8 128 No 0.01 wikitext 4096 13.65 GB No 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed.
gptq-8bit-128g-actorder_True 8 128 Yes 0.01 wikitext 4096 13.65 GB No 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy.
gptq-8bit-64g-actorder_True 8 64 Yes 0.01 wikitext 4096 13.95 GB No 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed.

How to download from branches

  • In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Redmond-Puffin-13B-GPTQ:main
  • With Git, you can clone a branch with:
git clone --single-branch --branch main https://huggingface.co/TheBloke/Redmond-Puffin-13B-GPTQ
  • In Python Transformers code, the branch is the revision parameter; see below.

How to easily download and use this model in text-generation-webui.

Please make sure you're using the latest version of text-generation-webui.

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

  1. Click the Model tab.
  2. Under Download custom model or LoRA, enter TheBloke/Redmond-Puffin-13B-GPTQ.
  • To download from a specific branch, enter for example TheBloke/Redmond-Puffin-13B-GPTQ:main
  • see Provided Files above for the list of branches for each option.
  1. Click Download.
  2. The model will start downloading. Once it's finished it will say "Done".
  3. In the top left, click the refresh icon next to Model.
  4. In the Model dropdown, choose the model you just downloaded: Redmond-Puffin-13B-GPTQ
  5. The model will automatically load, and is now ready for use!
  6. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
  • Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.
  1. Once you're ready, click the Text Generation tab and enter a prompt to get started!

How to use this GPTQ model from Python code

Install the necessary packages

Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .

For CodeLlama models only: you must use Transformers 4.33.0 or later.

If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:

pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git

You can then use the following code 

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/Redmond-Puffin-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''### human: {prompt}

### response:

'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.

ExLlama is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.

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!

Thanks to Clay from gpus.llm-utils.org!

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: NousResearch's Redmond Puffin 13B V1.3

Redmond-Puffin-13b-V1.3

The first commercially available language model released by Nous Research!

Redmond-Puffin-13B is likely the worlds first llama-2 based, fine-tuned language models, 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 Redmond AI for sponsoring the compute.

Special thank you to Emozilla for assisting with training experimentations and many issues encountered during training.

Notable mentions for assisting in some of the training issues goes to: Caseus and Teknium.

Model Training

Redmond-Puffin 13B-V1.3 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?

Puffin and Hermes-2 both beat previous SOTA for GPT4ALL benchmarks, with Hermes-2 winning by a 0.1% margin over Puffin.

  • Hermes 2 is trained on purely single turn instruction examples.

  • Puffin is trained mostly on multi-turn, long context, highly curated and cleaned GPT-4 conversations with real humans, as well as curated single-turn examples relating to Physics, Bio, Math and Chem.

For these reasons, it's reccomended to give Puffin a try if you want to have multi-turn conversations and/or long context communication.

Example Outputs!:

puffin

puffin

puffin

puffin

puffin

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.

Current Limitations

Some token mismatch problems and formatting issues have been idenitifed, these may very possibly effect the current output quality.

We plan to have these solved in an updated Puffin model in the very near future, please stay tuned!

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!

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)

note: After release, Puffin has since had its average GPT4All score beaten by 0.1%, by Nous' very own Model Hermes-2! Latest SOTA w/ Hermes 2- 70.0 (+0.1 over Puffins 69.9 score)

That being said, Puffin 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!

GPT4all :

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.4983|±  |0.0146|
|             |       |acc_norm|0.5068|±  |0.0146|
|arc_easy     |      0|acc     |0.7980|±  |0.0082|
|             |       |acc_norm|0.7757|±  |0.0086|
|boolq        |      1|acc     |0.8150|±  |0.0068|
|hellaswag    |      0|acc     |0.6132|±  |0.0049|
|             |       |acc_norm|0.8043|±  |0.0040|
|openbookqa   |      0|acc     |0.3560|±  |0.0214|
|             |       |acc_norm|0.4560|±  |0.0223|
|piqa         |      0|acc     |0.7954|±  |0.0094|
|             |       |acc_norm|0.8069|±  |0.0092|
|winogrande   |      0|acc     |0.7245|±  |0.0126|

|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5368|±  |0.0363|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7127|±  |0.0236|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3023|±  |0.0286|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1003|±  |0.0159|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2520|±  |0.0194|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.1743|±  |0.0143|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4200|±  |0.0285|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2900|±  |0.0203|
|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5430|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4442|±  |0.0235|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2074|±  |0.0128|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.5083|±  |0.0373|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.4970|±  |0.0159|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3260|±  |0.0148|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2136|±  |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1326|±  |0.0081|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4200|±  |0.0285|

AGI Eval:

|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2283|±  |0.0264|
|                              |       |acc_norm|0.2244|±  |0.0262|
|agieval_logiqa_en             |      0|acc     |0.2780|±  |0.0176|
|                              |       |acc_norm|0.3164|±  |0.0182|
|agieval_lsat_ar               |      0|acc     |0.2348|±  |0.0280|
|                              |       |acc_norm|0.2043|±  |0.0266|
|agieval_lsat_lr               |      0|acc     |0.3392|±  |0.0210|
|                              |       |acc_norm|0.2961|±  |0.0202|
|agieval_lsat_rc               |      0|acc     |0.4387|±  |0.0303|
|                              |       |acc_norm|0.3569|±  |0.0293|
|agieval_sat_en                |      0|acc     |0.5874|±  |0.0344|
|                              |       |acc_norm|0.5194|±  |0.0349|
|agieval_sat_en_without_passage|      0|acc     |0.4223|±  |0.0345|
|                              |       |acc_norm|0.3447|±  |0.0332|
|agieval_sat_math              |      0|acc     |0.3364|±  |0.0319|
|                              |       |acc_norm|0.2773|±  |0.0302|