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Platypus2 70B Instruct - GGUF

Model creator: garage-bAInd
Original model: Platypus2 70B Instruct

Description

This repo contains GGUF format model files for garage-bAInd's Platypus2 70B Instruct.

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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

Repositories available

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Licensing

The creator of the source model has listed its license as cc-by-nc-4.0, 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: garage-bAInd's Platypus2 70B Instruct.

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221

They are also compatible with many third party UIs and libraries - please see the list at the top of this 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
platypus2-70b-instruct.Q2_K.gguf	Q2_K	2	29.28 GB	31.78 GB	smallest, significant quality loss - not recommended for most purposes
platypus2-70b-instruct.Q3_K_S.gguf	Q3_K_S	3	29.92 GB	32.42 GB	very small, high quality loss
platypus2-70b-instruct.Q3_K_M.gguf	Q3_K_M	3	33.19 GB	35.69 GB	very small, high quality loss
platypus2-70b-instruct.Q3_K_L.gguf	Q3_K_L	3	36.15 GB	38.65 GB	small, substantial quality loss
platypus2-70b-instruct.Q4_0.gguf	Q4_0	4	38.87 GB	41.37 GB	legacy; small, very high quality loss - prefer using Q3_K_M
platypus2-70b-instruct.Q4_K_S.gguf	Q4_K_S	4	39.07 GB	41.57 GB	small, greater quality loss
platypus2-70b-instruct.Q4_K_M.gguf	Q4_K_M	4	41.42 GB	43.92 GB	medium, balanced quality - recommended
platypus2-70b-instruct.Q5_0.gguf	Q5_0	5	47.46 GB	49.96 GB	legacy; medium, balanced quality - prefer using Q4_K_M
platypus2-70b-instruct.Q5_K_S.gguf	Q5_K_S	5	47.46 GB	49.96 GB	large, low quality loss - recommended
platypus2-70b-instruct.Q5_K_M.gguf	Q5_K_M	5	48.75 GB	51.25 GB	large, very low quality loss - recommended
platypus2-70b-instruct.Q6_K.gguf	Q6_K	6	56.59 GB	59.09 GB	very large, extremely low quality loss
platypus2-70b-instruct.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:

platypus2-70b-instruct.Q6_K.gguf-split-a
platypus2-70b-instruct.Q6_K.gguf-split-b

q8_0

Please download:

platypus2-70b-instruct.Q8_0.gguf-split-a
platypus2-70b-instruct.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat platypus2-70b-instruct.Q6_K.gguf-split-* > platypus2-70b-instruct.Q6_K.gguf && rm platypus2-70b-instruct.Q6_K.gguf-split-*
cat platypus2-70b-instruct.Q8_0.gguf-split-* > platypus2-70b-instruct.Q8_0.gguf && rm platypus2-70b-instruct.Q8_0.gguf-split-*

Windows command line:

COPY /B platypus2-70b-instruct.Q6_K.gguf-split-a + platypus2-70b-instruct.Q6_K.gguf-split-b platypus2-70b-instruct.Q6_K.gguf
del platypus2-70b-instruct.Q6_K.gguf-split-a platypus2-70b-instruct.Q6_K.gguf-split-b

COPY /B platypus2-70b-instruct.Q8_0.gguf-split-a + platypus2-70b-instruct.Q8_0.gguf-split-b platypus2-70b-instruct.Q8_0.gguf
del platypus2-70b-instruct.Q8_0.gguf-split-a platypus2-70b-instruct.Q8_0.gguf-split-b

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

LM Studio
LoLLMS Web UI
Faraday.dev

In `text-generation-webui`

Under Download Model, you can enter the model repo: TheBloke/Platypus2-70B-Instruct-GGUF and below it, a specific filename to download, such as: platypus2-70b-instruct.q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub>=0.17.1

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download TheBloke/Platypus2-70B-Instruct-GGUF platypus2-70b-instruct.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/Platypus2-70B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Platypus2-70B-Instruct-GGUF platypus2-70b-instruct.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows CLI users: Use set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 before running the download command.

Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.

./main -ngl 32 -m platypus2-70b-instruct.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"

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 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

How to run in `text-generation-webui`

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

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model from Python using ctransformers

First install the package

# 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

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/Platypus2-70B-Instruct-GGUF", model_file="platypus2-70b-instruct.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:

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.

Patreon: https://patreon.com/TheBlokeAI
Ko-Fi: https://ko-fi.com/TheBlokeAI

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: garage-bAInd's Platypus2 70B Instruct

Platypus2-70B-instruct

Platypus-70B-instruct is a merge of garage-bAInd/Platypus2-70B and upstage/Llama-2-70b-instruct-v2.

Benchmark Metrics

Metric	Value
MMLU (5-shot)	70.48
ARC (25-shot)	71.84
HellaSwag (10-shot)	87.94
TruthfulQA (0-shot)	62.26
Avg.	73.13

We use state-of-the-art Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.

Model Details

Trained by: Platypus2-70B trained by Cole Hunter & Ariel Lee; Llama-2-70b-instruct trained by upstageAI
Model type: Platypus2-70B-instruct is an auto-regressive language model based on the LLaMA 2 transformer architecture.
Language(s): English
License: Non-Commercial Creative Commons license (CC BY-NC-4.0)

Prompt Template

### Instruction:

<prompt> (without the <>)

### Response:

Training Dataset

garage-bAInd/Platypus2-70B trained using STEM and logic based dataset garage-bAInd/Open-Platypus.

Please see our paper and project webpage for additional information.

Training Procedure

garage-bAInd/Platypus2-70B was instruction fine-tuned using LoRA on 8 A100 80GB. For training details and inference instructions please see the Platypus GitHub repo.

Reproducing Evaluation Results

Install LM Evaluation Harness:

# clone repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# change to repo directory
cd lm-evaluation-harness
# check out the correct commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# install
pip install -e .

Each task was evaluated on a single A100 80GB GPU.

ARC:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/arc_challenge_25shot.json --device cuda --num_fewshot 25

HellaSwag:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/hellaswag_10shot.json --device cuda --num_fewshot 10

MMLU:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/mmlu_5shot.json --device cuda --num_fewshot 5

TruthfulQA:

python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/truthfulqa_0shot.json --device cuda

Limitations and bias

Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/

Citations

@article{platypus2023,
    title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
    author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
    booktitle={arXiv preprint arxiv:2308.07317},
    year={2023}
}

@misc{touvron2023llama,
    title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
    author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov       year={2023},
    eprint={2307.09288},
    archivePrefix={arXiv},
}

@inproceedings{
    hu2022lora,
    title={Lo{RA}: Low-Rank Adaptation of Large Language Models},
    author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=nZeVKeeFYf9}
}

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GGUF

Model size

69B params

Architecture

llama

2-bit

3-bit

4-bit

5-bit

Inference API

Inference API (serverless) has been turned off for this model.

Model tree for TheBloke/Platypus2-70B-Instruct-GGUF

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Platypus2-70B-Instruct-GGUF

Platypus2 70B Instruct - GGUF

Description

About GGUF

Repositories available

Prompt template: Alpaca

Licensing

Compatibility

Explanation of quantisation methods

Provided files

Q6_K and Q8_0 files are split and require joining

q6_K

q8_0

How to download GGUF files

In `text-generation-webui`

On the command line, including multiple files at once

Example `llama.cpp` command

How to run in `text-generation-webui`

How to run from Python code

How to load this model from Python using ctransformers

First install the package

Simple example code to load one of these GGUF models

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: garage-bAInd's Platypus2 70B Instruct

Platypus2-70B-instruct

Benchmark Metrics

Model Details

Prompt Template

Training Dataset

Training Procedure

Reproducing Evaluation Results

Limitations and bias

Citations

Model tree for TheBloke/Platypus2-70B-Instruct-GGUF

Datasets used to train TheBloke/Platypus2-70B-Instruct-GGUF

Platypus2 70B Instruct - GGUF

Description

About GGUF

Repositories available

Prompt template: Alpaca

Licensing

Compatibility

Explanation of quantisation methods

Provided files

Q6_K and Q8_0 files are split and require joining

q6_K

q8_0

How to download GGUF files

In text-generation-webui

On the command line, including multiple files at once

Example llama.cpp command

How to run in text-generation-webui

How to run from Python code

How to load this model from Python using ctransformers

First install the package

Simple example code to load one of these GGUF models

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: garage-bAInd's Platypus2 70B Instruct

Platypus2-70B-instruct

Benchmark Metrics

Model Details

Prompt Template

Training Dataset

Training Procedure

Reproducing Evaluation Results

Limitations and bias

Citations

Model tree for TheBloke/Platypus2-70B-Instruct-GGUF

Datasets used to train TheBloke/Platypus2-70B-Instruct-GGUF

In `text-generation-webui`

Example `llama.cpp` command

How to run in `text-generation-webui`