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

DiscoLM German 7B v1 - GGUF


This repo contains GGUF format model files for Disco Research's DiscoLM German 7B v1.

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

Prompt template: ChatML



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_german_7b_v1.Q2_K.gguf Q2_K 2 2.72 GB 5.22 GB significant quality loss - not recommended for most purposes
discolm_german_7b_v1.Q3_K_S.gguf Q3_K_S 3 3.16 GB 5.66 GB very small, high quality loss
discolm_german_7b_v1.Q3_K_M.gguf Q3_K_M 3 3.52 GB 6.02 GB very small, high quality loss
discolm_german_7b_v1.Q3_K_L.gguf Q3_K_L 3 3.82 GB 6.32 GB small, substantial quality loss
discolm_german_7b_v1.Q4_0.gguf Q4_0 4 4.11 GB 6.61 GB legacy; small, very high quality loss - prefer using Q3_K_M
discolm_german_7b_v1.Q4_K_S.gguf Q4_K_S 4 4.14 GB 6.64 GB small, greater quality loss
discolm_german_7b_v1.Q4_K_M.gguf Q4_K_M 4 4.37 GB 6.87 GB medium, balanced quality - recommended
discolm_german_7b_v1.Q5_0.gguf Q5_0 5 5.00 GB 7.50 GB legacy; medium, balanced quality - prefer using Q4_K_M
discolm_german_7b_v1.Q5_K_S.gguf Q5_K_S 5 5.00 GB 7.50 GB large, low quality loss - recommended
discolm_german_7b_v1.Q5_K_M.gguf Q5_K_M 5 5.13 GB 7.63 GB large, very low quality loss - recommended
discolm_german_7b_v1.Q6_K.gguf Q6_K 6 5.94 GB 8.44 GB very large, extremely low quality loss
discolm_german_7b_v1.Q8_0.gguf Q8_0 8 7.70 GB 10.20 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.

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_German_7b_v1-GGUF and below it, a specific filename to download, such as: discolm_german_7b_v1.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_German_7b_v1-GGUF discolm_german_7b_v1.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_German_7b_v1-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_German_7b_v1-GGUF discolm_german_7b_v1.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_german_7b_v1.Q4_K_M.gguf --color -c 32768 --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 32768 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:
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_german_7b_v1.Q4_K_M.gguf",  # Download the model file first
  n_ctx=32768,  # 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_german_7b_v1.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
    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:


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: 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 German 7B v1

DiscoLM German 7b v1


Table of Contents

  1. Introduction
  2. Demo
  4. Prompt Format
  5. Results
  6. Evaluation
  7. Dataset
  8. Limitations & Biases
  9. Acknowledgements
  10. About DiscoResearch
  11. Disclaimer


DiscoLM German 7b is a Mistral-based large language model with a focus on German-language applications and the successor of the EM German model family. It was trained on a large dataset of instructions in German and English with a SFT finetuning phase followed by additional DPO reinforcement learning. The model is optimized for German text, providing proficiency in understanding, generating, and interacting with German language content while preserving its fluency in English and excelling at translation tasks.

Our goal with Disco LM German was not to beat benchmarks, but to provide a robust and reliable model for everyday use that can serve as a drop-in replacement for ChatGPT and other proprietary models. We find that the perceived quality of it´s German-language output is even higher than GPT-4 in many cases; however it won't compete with larger models and top English 7b models for very complex reasoning, math or coding tasks.


Please find a Demo and try the model at demo.discoresearch.org (in case the Demo is down and you have questions, you can contact us on our Discord).


Model Links

We will update the links as soon as the quants are available on HuggingFace.

DiscoLM German 7b v1 Link Link Link Link

Prompt Format

DiscoLM German uses ChatML as the prompt format which enables OpenAI endpoint compatability and is supported by most inference libraries and frontends.

System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.

Du bist ein hilfreicher Assistent.<|im_end|>
Wer bist du?<|im_end|>
Ich bin ein Sprachmodell namens DiscoLM German und ich wurde von DiscoResearch trainiert.<|im_end|>

This prompt is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [
    {"role": "system", "content": "Du bist ein hilfreicher Assistent."},
    {"role": "user", "content": "Wer bist du?"}
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")

When tokenizing messages for generation, set add_generation_prompt=True when calling apply_chat_template(). This will append <|im_start|>assistant\n to your prompt, to ensure that the model continues with an assistant response.

Retrieval Format

You can use a special retrieval format to improve steerability and reduce hallucinations for RAG applications (but other, more default formats should also work, this is purely optional)


### System:

Du bist ein hilfreicher Assistent. Für die folgende Aufgabe stehen dir zwischen den Tags BEGININPUT und ENDINPUT mehrere Quellen zur Verfügung. Metadaten zu den einzelnen Quellen wie Autor, URL o.ä. sind zwischen BEGINCONTEXT und ENDCONTEXT zu finden, danach folgt der Text der Quelle. Die eigentliche Aufgabe oder Frage ist zwischen BEGININSTRUCTION und ENDINSTRUCTION zu finden. Beantworte diese ausschließlich mit Informationen aus den gegebenen Quellen und gebe die Information zur genutzten Quelle  unter "Quelle:" an. Sollten die Quellen keine relevanten Informationen enthalten, antworte: "Mit den gegebenen Informationen ist diese Frage nicht zu beantworten."

### User Prompt:

url: https://this.is.fake.news
time: 2089-09-01
Buxtehude ist die größte Stadt Deutschlands mit 96.56 Millionen Einwohnern.

Was ist die größte deutsche Stadt?

### Model Answer:

Die größte deutsche Stadt ist Buxtehude.

  url: https://this.is.fake.news
  time: 2089-09-01

Function Calling

The model also supports structured outputs/function calling, albeit this is a very experimental feature and YMMV. This will be improved in the future.

The model will prefix functioncalls with <functioncall> and you can provide results in response with <functionresponse> for Multi-Turn applications.


### System:

Du bist ein hilfreicher Assistent. Extrahiere alle Personen aus den Eingaben des Users.

Du hast Zugriff auf folgende Funktionen:

{'name': 'PersonList',
 'description': 'Extrahiere die Namen aller im Text vorkommenden Personen',
 'parameters': {'$defs': {'Person': {'description': 'Details über eine person',
    'properties': {'name': {'title': 'Name', 'type': 'string'},
     'job': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Job'},
     'age': {'anyOf': [{'type': 'integer'}, {'type': 'null'}],
      'title': 'Age'}},
    'required': ['name', 'job', 'age'],
    'title': 'Person',
    'type': 'object'}},
  'properties': {'person_list': {'items': {'$ref': '#/$defs/Person'},
    'title': 'Person List',
    'type': 'array'}},
  'required': ['person_list'],
  'type': 'object'}}

### User Prompt:

Björn (25) und Jan sind die Gründer von ellamind.

### Model Answer:

<functioncall> {"name": "PersonList", "arguments": '{"person_list": ["{"name": "Björn", "job": "founder", "age": 25}, {"name": "Jan", "job": "founder", "age": null}]}'}


-to follow -


As written above, we believe that current benchmarks don't capture the full spectrum of LLM capabilities very well. We didn't look at any benchmark results (besides training losses) until the work on DiscoLM was finished and didn't include any data resembling common benchmark formats in our training data.

That said, preliminary results with a German version of MT Bench show promising results: While lacking for coding and extraxtion tasks, DiscoLM German 7b performs not far below GPT-3.5-turbo on many tasks and even singificantly outperforms it in the reasoning category.


Additional Benchmark results will follow. The biggest strength of this model (language quality as perceived by native speakers) can't yet be captured in a benchmark - please let us know if you have an idea how to change this!


The dataset is a mixture of multi-turn chats, retrieval instructions and synthetically generated instructions spawning many topics and applications.

Limitations & Biases

This model can produce factually incorrect and offensive output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate biased or otherwise offensive outputs and it is the responsibility of the user to implement a safety/moderation layer. Please use with caution.


DiscoLM German is a DiscoResearch project led by JP Harries and supported by Björn Plüster and Daniel Auras.

We thank HessianAI for providing compute & support for various DiscoResearch projects and our friends at LAION for their work on LeoLM and scientific adivce.**

Development of DiscoLM German 7b was sponsored by ellamind, where some of our founders are working on creating customized models for business applications with a focus on non-english language applications. Please get in contact if you need customized models for your business!

Built with Axolotl

About DiscoResearch

DiscoResearch is an aspiring open research community for AI enthusiasts and LLM hackers. Come join our Discord, share your opinions and ideas, and advance open LLM research with us!


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 deployed with additional safety measures in place.

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