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.
These files were quantised using hardware kindly provided by Massed Compute.
This may not be a complete list; if you know of others, please let me know!
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.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
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 calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration 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 and Mistral models in 4-bit.
If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.
Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)
Python code example: inference from this GPTQ model
Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
shell
1pip3 install --upgrade transformers optimum
2# If using PyTorch 2.1 + CUDA 12.x:3pip3 install --upgrade auto-gptq
4# or, if using PyTorch 2.1 + CUDA 11.x:5pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
1from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
23model_name_or_path ="TheBloke/DiscoLM_German_7b_v1-GPTQ"4# To use a different branch, change revision5# For example: revision="gptq-4bit-32g-actorder_True"6model = AutoModelForCausalLM.from_pretrained(model_name_or_path,7 device_map="auto",8 trust_remote_code=False,9 revision="main")1011tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)1213prompt ="Write a story about llamas"14system_message ="You are a story writing assistant"15prompt_template=f'''<|im_start|>system
16{system_message}<|im_end|>
17<|im_start|>user
18{prompt}<|im_end|>
19<|im_start|>assistant
20'''2122print("\n\n*** Generate:")2324input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()25output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)26print(tokenizer.decode(output[0]))2728# Inference can also be done using transformers' pipeline2930print("*** Pipeline:")31pipe = pipeline(32"text-generation",33 model=model,34 tokenizer=tokenizer,35 max_new_tokens=512,36 do_sample=True,37 temperature=0.7,38 top_p=0.95,39 top_k=40,40 repetition_penalty=1.141)4243print(pipe(prompt_template)[0]['generated_text'])
Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
ExLlama is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
Discord
For further support, and discussions on these models and AI in general, join us at:
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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.
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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 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.
Demo
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).
Downloads
Model Links
We will update the links as soon as the quants are available on HuggingFace.
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.
<|im_start|>system
Du bist ein hilfreicher Assistent.<|im_end|>
<|im_start|>user
Wer bist du?<|im_end|>
<|im_start|>assistant
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:
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)
Example:
### 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:
BEGININPUT
BEGINCONTEXT
url: https://this.is.fake.news
time: 2089-09-01
ENDCONTEXT
Buxtehude ist die größte Stadt Deutschlands mit 96.56 Millionen Einwohnern.
ENDINPUT
BEGININSTRUCTION
Was ist die größte deutsche Stadt?
ENDINSTRUCTION
### Model Answer:
Die größte deutsche Stadt ist Buxtehude.
Quelle:
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.
Example:
### 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}]}'}
Results
-to follow -
Evaluation
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.
MTBench_DE_Results
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!
Dataset
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.
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!
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!
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 deployed with additional safety measures in place.
