TheBloke/DiscoLM-70B-GGUF

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

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DiscoLM 70B - GGUF

• Model creator: Disco Research
• Original model: DiscoLM 70B

Description

This repo contains GGUF format model files for Disco Research's DiscoLM 70B.

These files were quantised using hardware kindly provided by Massed Compute.

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.

Here is an incomplete 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.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• 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.
• 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.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Repositories available

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Disco Research's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

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

• discolm-70b.Q6_K.gguf-split-a
• discolm-70b.Q6_K.gguf-split-b

q8_0

Please download:

• discolm-70b.Q8_0.gguf-split-a
• discolm-70b.Q8_0.gguf-split-b

To join the files, do the following:

Linux and macOS:

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

Windows command line:

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

COPY /B discolm-70b.Q8_0.gguf-split-a + discolm-70b.Q8_0.gguf-split-b discolm-70b.Q8_0.gguf
del discolm-70b.Q8_0.gguf-split-a discolm-70b.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/DiscoLM-70B-GGUF and below it, a specific filename to download, such as: discolm-70b.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

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

huggingface-cli download TheBloke/DiscoLM-70B-GGUF discolm-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

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

huggingface-cli download TheBloke/DiscoLM-70B-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:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/DiscoLM-70B-GGUF discolm-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m discolm-70b.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 8192 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

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 can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama

# 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 = Llama(
  model_path="./discolm-70b.Q4_K_M.gguf",  # Download the model file first
  n_ctx=8192,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./discolm-70b.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

• LangChain + llama-cpp-python
• LangChain + ctransformers

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: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Disco Research's DiscoLM 70B

DiscoLM 70b

DiscoLM 70b is a 70b model based on Laion's LeoLM 70b which underwent additional continued pretraining for 65b tokens of German text, strengthening it's multilingual capabilities while retaining (and partially improving) English capabilities. This was then further finetuned on a combination of some the most popular open-source instruction sets. DiscoLM 70b is a DiscoResearch project and was trained by Björn Plüster.

The model was trained with compute provided by HessianAI in collaboration with LAION - we are very grateful for their support; please check out their wesbite and projects!

Table of Contents

1. Download
2. Benchmarks
3. Prompt Format
4. Dataset
5. Acknowledgements
6. Contact
7. About DiscoResearch
8. Disclaimer

Download

Huggingface	GPTQ	GGUF	AWQ	Base Model
Link	@TheBloke	@TheBloke	@TheBloke	LeoLM 70b

Benchmarks

Hugginface Leaderboard

This models is still an early Alpha and we can't guarantee that there isn't any contamination. However, the average of 71.24 would earn the #3 spot on the HF leaderboard at the time of writing.

Metric	Value
ARC (25-shot)	68.77
HellaSwag (10-shot)	85.41
MMLU (5-shot)	68.64
TruthfulQA (0-shot)	57.69
Winogrande (5-shot)	83.27
GSM8k (5-shot)	63.68
Avg.	71.24

We use Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.

FastEval

Metric	Value
GSM8K	70.6
Math	17.8
BBH	63.4
MMLU	64.7
Avg.	48.87

Screenshot of the current (sadly no longer maintained) FastEval CoT leaderboard:

MTBench

{
    "first_turn": 7.9,
    "second_turn": 7.0625,
    "categories": {
        "writing": 9.55,
        "roleplay": 8.35,
        "reasoning": 6.15,
        "math": 4.7,
        "coding": 4.8,
        "extraction": 7.35,
        "stem": 9.1,
        "humanities": 9.85
    },
    "average": 7.48125
}

Screenshot of the current FastEval MT Bench leaderboard:

Prompt Format

This model follows the ChatML format:

<|im_start|>system
You are DiscoLM, a helpful assistant.
<|im_end|>
<|im_start|>user
Please tell me possible reasons to call a research collective "Disco Research"<|im_end|>
<|im_start|>assistant

This formatting is also available via a pre-defined Transformers chat template, which means that lists of messages can be formatted for you with the apply_chat_template() method:

chat = [
  {"role": "system", "content": "You are DiscoLM, a helpful assistant."},
  {"role": "user", "content": "Please tell me possible reasons to call a research collective Disco Research"}
]
tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

If you use tokenize=True and return_tensors="pt" instead, then you will get a tokenized and formatted conversation ready to pass to model.generate().

Dataset

The dataset curation for DiscoLM 70b followed a "brute force"/"PoC" approach.

The following datasets were used for training DiscoLM 70b:

• SlimOrca-Dedup
• OpenSchnabeltier translated to DE from OpenPlatypus
• OpenHermes
• MetaMathQA
• UltraChat DE translated to DE from UltraChat
• Synthia v.1.3
• German_Songs
• German_Poems
• Capybara Dataset by Nous Research

Many thanks for all dataset providers/curators!

Contact

Best way to reach us is on our Discord.

About DiscoResearch

DiscoResearch is an aspiring open research community. Disco should be a place where researchers from many communities can come together to combine their expertise and create innovative and groundbreaking LLMs. Come join our Discord, share your opinions and ideas, and advance open LLM research with us!

Acknowledgements

Disco 70b is a DiscoResearch project and was trained by Björn Plüster. Jan Harries helped with technical adivce, logistics and the Model Card. AutoMeta also provided helpful technical advice and rounded up his connections to select a set of high-quality datasets. The model was trained with compute provided by HessianAI in collaboration with LAION - many thanks in particular to Patrick Schramowski for his support.

We are standing on the shoulders of giants; many thanks in no particular order to Laion for LeoLM 70b (especially to Christoph Schuhmann who got us all connected), TheBloke for providing quantized versions, winglian for Axolotl which was used to train the model and the SlimOrca dataset, garage-bAInd, Teknium, Migel Tissera, MetaMath for their great datasets (please contact us if we forgot to mention you here!).

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply.