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WizardLM-70B-V1.0-GGUF

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GGUF
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TheBlokeAI

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

WizardLM 70B V1.0 - GGUF

Description

This repo contains GGUF format model files for WizardLM's WizardLM 70B V1.0.

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: Vicuna 

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:

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
wizardlm-70b-v1.0.Q2_K.gguf Q2_K 2 29.28 GB 31.78 GB smallest, significant quality loss - not recommended for most purposes
wizardlm-70b-v1.0.Q3_K_S.gguf Q3_K_S 3 29.92 GB 32.42 GB very small, high quality loss
wizardlm-70b-v1.0.Q3_K_M.gguf Q3_K_M 3 33.19 GB 35.69 GB very small, high quality loss
wizardlm-70b-v1.0.Q3_K_L.gguf Q3_K_L 3 36.15 GB 38.65 GB small, substantial quality loss
wizardlm-70b-v1.0.Q4_0.gguf Q4_0 4 38.87 GB 41.37 GB legacy; small, very high quality loss - prefer using Q3_K_M
wizardlm-70b-v1.0.Q4_K_S.gguf Q4_K_S 4 39.07 GB 41.57 GB small, greater quality loss
wizardlm-70b-v1.0.Q4_K_M.gguf Q4_K_M 4 41.42 GB 43.92 GB medium, balanced quality - recommended
wizardlm-70b-v1.0.Q5_0.gguf Q5_0 5 47.46 GB 49.96 GB legacy; medium, balanced quality - prefer using Q4_K_M
wizardlm-70b-v1.0.Q5_K_S.gguf Q5_K_S 5 47.46 GB 49.96 GB large, low quality loss - recommended
wizardlm-70b-v1.0.Q5_K_M.gguf Q5_K_M 5 48.75 GB 51.25 GB large, very low quality loss - recommended
wizardlm-70b-v1.0.Q6_K.gguf Q6_K 6 56.59 GB 59.09 GB very large, extremely low quality loss
wizardlm-70b-v1.0.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:

  • wizardlm-70b-v1.0.Q6_K.gguf-split-a
  • wizardlm-70b-v1.0.Q6_K.gguf-split-b

q8_0

Please download:

  • wizardlm-70b-v1.0.Q8_0.gguf-split-a
  • wizardlm-70b-v1.0.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

cat wizardlm-70b-v1.0.Q6_K.gguf-split-* > wizardlm-70b-v1.0.Q6_K.gguf && rm wizardlm-70b-v1.0.Q6_K.gguf-split-*
cat wizardlm-70b-v1.0.Q8_0.gguf-split-* > wizardlm-70b-v1.0.Q8_0.gguf && rm wizardlm-70b-v1.0.Q8_0.gguf-split-*

Windows command line:

COPY /B wizardlm-70b-v1.0.Q6_K.gguf-split-a + wizardlm-70b-v1.0.Q6_K.gguf-split-b wizardlm-70b-v1.0.Q6_K.gguf
del wizardlm-70b-v1.0.Q6_K.gguf-split-a wizardlm-70b-v1.0.Q6_K.gguf-split-b

COPY /B wizardlm-70b-v1.0.Q8_0.gguf-split-a + wizardlm-70b-v1.0.Q8_0.gguf-split-b wizardlm-70b-v1.0.Q8_0.gguf
del wizardlm-70b-v1.0.Q8_0.gguf-split-a wizardlm-70b-v1.0.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/WizardLM-70B-V1.0-GGUF and below it, a specific filename to download, such as: wizardlm-70b-v1.0.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/WizardLM-70B-V1.0-GGUF wizardlm-70b-v1.0.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/WizardLM-70B-V1.0-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/WizardLM-70B-V1.0-GGUF wizardlm-70b-v1.0.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 wizardlm-70b-v1.0.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"

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/WizardLM-70B-V1.0-GGUF", model_file="wizardlm-70b-v1.0.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.

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 BjΓ€reholt, 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, 쀀ꡐ κΉ€, 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: WizardLM's WizardLM 70B V1.0

WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

πŸ€— HF Repo β€’πŸ± Github Repo β€’ 🐦 Twitter β€’ πŸ“ƒ [WizardLM] β€’ πŸ“ƒ [WizardCoder] β€’ πŸ“ƒ [WizardMath]

πŸ‘‹ Join our Discord

Unofficial Video Introductions

Thanks to the enthusiastic friends, their video introductions are more lively and interesting.

  1. NEW WizardLM 70b πŸ”₯ Giant Model...Insane Performance
  2. GET WizardLM NOW! 7B LLM KING That Can Beat ChatGPT! I'm IMPRESSED!
  3. WizardLM: Enhancing Large Language Models to Follow Complex Instructions
  4. WizardCoder AI Is The NEW ChatGPT's Coding TWIN!

News

  • πŸ”₯πŸ”₯πŸ”₯[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
  • [2023/06/16] We released WizardCoder-15B-V1.0 , which surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
Model Checkpoint Paper HumanEval MBPP Demo License
WizardCoder-Python-34B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 73.2 61.2 Demo Llama2
WizardCoder-15B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 59.8 50.6 -- OpenRAIL-M
WizardCoder-Python-13B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 64.0 55.6 -- Llama2
WizardCoder-Python-7B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 55.5 51.6 Demo Llama2
WizardCoder-3B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 34.8 37.4 -- OpenRAIL-M
WizardCoder-1B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardCoder] 23.8 28.6 -- OpenRAIL-M
  • πŸ”₯ [08/11/2023] We release WizardMath Models.
  • πŸ”₯ Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
  • πŸ”₯ Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
  • πŸ”₯ Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.
Model Checkpoint Paper GSM8k MATH Online Demo License
WizardMath-70B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardMath] 81.6 22.7 Demo Llama 2
WizardMath-13B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardMath] 63.9 14.0 Demo Llama 2
WizardMath-7B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardMath] 54.9 10.7 Demo Llama 2
Model Checkpoint Paper MT-Bench AlpacaEval GSM8k HumanEval License
WizardLM-70B-V1.0 πŸ€— HF Link πŸ“ƒComing Soon 7.78 92.91% 77.6% 50.6 pass@1 Llama 2 License
WizardLM-13B-V1.2 πŸ€— HF Link 7.06 89.17% 55.3% 36.6 pass@1 Llama 2 License
WizardLM-13B-V1.1 πŸ€— HF Link 6.76 86.32% 25.0 pass@1 Non-commercial
WizardLM-30B-V1.0 πŸ€— HF Link 7.01 37.8 pass@1 Non-commercial
WizardLM-13B-V1.0 πŸ€— HF Link 6.35 75.31% 24.0 pass@1 Non-commercial
WizardLM-7B-V1.0 πŸ€— HF Link πŸ“ƒ [WizardLM] 19.1 pass@1 Non-commercial
  • πŸ”₯πŸ”₯πŸ”₯ [08/09/2023] We released WizardLM-70B-V1.0 model.

Github Repo: https://github.com/nlpxucan/WizardLM

Twitter: https://twitter.com/WizardLM_AI/status/1689270108747976704

Discord: https://discord.gg/bpmeZD7V

❗Note for model system prompts usage:

WizardLM adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>......

Inference WizardLM Demo Script

We provide the inference WizardLM demo code here.

Please cite the paper if you use the data or code from WizardLM.

@article{xu2023wizardlm,
  title={Wizardlm: Empowering large language models to follow complex instructions},
  author={Xu, Can and Sun, Qingfeng and Zheng, Kai and Geng, Xiubo and Zhao, Pu and Feng, Jiazhan and Tao, Chongyang and Jiang, Daxin},
  journal={arXiv preprint arXiv:2304.12244},
  year={2023}
}

❗To commen concern about dataset:

Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models.

Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .

Our researchers have no authority to publicly release them without authorization.

Thank you for your understanding.

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