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openGPT-X/
Teuken-7B-instruct-v0.6

Name: Teuken-7B-instruct-v0.6
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Model Card for Teuken 7B-instruct-v0.6

Teuken 7B-instruct-v0.6 is an instruction-tuned 7B parameter multilingual large language model (LLM) pre-trained with 6T tokens in all official 24 European languages and released in the research project OpenGPT-X. The base model Teuken 7B-base-v0.6 is also available.

Model Description

Developed by: Fraunhofer, Forschungszentrum Jülich, TU Dresden, DFKI
Funded by: German Federal Ministry of Economics and Climate Protection (BMWK) in the context of the OpenGPT-X project
Model type: Transformer based decoder-only model
Language(s) (NLP): bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
Shared by: OpenGPT-X

Uses

Teuken 7B-instruct-v0.6 is designed for private, non-commercial, research, and educational use in all 24 official European Union languages. Its multilingual training makes it particularly well-suited for tasks requiring stable performance across these languages. Unlike English-centric models, Teuken 7B-instruct-v0.6 aims to reflect European linguistic diversity and values, offering more balanced and culturally aligned responses. This specialization makes it a strong choice for applications in multilingual and Europe-focused settings.

Disclaimer Toxic Content:

This Large Language Model (LLM) may generate content that is inappropriate, offensive, or harmful. While the dataset has been filtered to minimize such outputs, the model may still produce text that is biased or toxic due to the large scale and diverse nature of the data.

Out-of-Scope Use

The model is not intended for use in math and coding tasks.

Bias, Risks, and Limitations

Teuken 7B-instruct-v0.6 is an instruction-tuned version of Teuken 7B-instruct-v0.6 that is not completely free from biases and hallucinations.

How to Get Started with the Model

Usage

The model requires a few libraries that can be installed in your python environment:

python -m pip install numpy torch huggingface_hub transformers sentencepiece

After installation, here's an example of how to use the model:

As this model is a fine-tuned model, it must be used with the provided prompt template. Using the model without the prompt template is not intended and is not recommended. The prompt template is defined as follows:

python

user="Hi!"
lang_code = "DE"
system_messages={
            "EN": "A chat between a human and an artificial intelligence assistant."
            " The assistant gives helpful and polite answers to the human's questions.",
            "DE": "Ein Gespräch zwischen einem Menschen und einem Assistenten mit künstlicher Intelligenz."
            " Der Assistent gibt hilfreiche und höfliche Antworten auf die Fragen des Menschen.",
        }
 
prompt = f"System: {system_messages[lang_code]}\nUser: {user}\nAssistant:"

The prompt template is also directly integrated in the Tokenizer and can be used as follows:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "openGPT-X/Teuken-7B-instruct-v0.6"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
model = model.to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    use_fast=False,
    trust_remote_code=True,
)
messages = [{"role": "User", "content": "Wer bist du?"}]
prompt_ids = tokenizer.apply_chat_template(messages, chat_template="DE", tokenize=True, add_generation_prompt=True, return_tensors="pt")
prediction = model.generate(
    prompt_ids.to(model.device),
    max_length=512,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    temperature=0.7,
    num_return_sequences=1,
)
prediction_text = tokenizer.decode(prediction[0].tolist())
print(prediction_text)

This example demonstrates how to load the model and tokenizer, prepare input, generate text, and print the result.

Usage with vLLM Server

Starting the vLLM Server:

vllm serve openGPT-X/Teuken-7B-instruct-v0.6 --trust-remote-code

Use Chat API with vLLM and pass the language of the Chat-Template as extra body:

python

from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1",
)
completion = client.chat.completions.create(
    model="openGPT-X/Teuken-7B-instruct-commercial-v0.4",
    messages=[{"role": "User", "content": "Hallo"}],
    extra_body={"chat_template":"DE"}
)
print(f"Assistant: {completion]")

The default language of the Chat-Template can also be set when starting the vLLM Server. For this create a new file with the name lang and the content DE and start the vLLM Server as follows:

vllm serve openGPT-X/Teuken-7B-instruct-v0.6 --trust-remote-code --chat-template lang

Usage with vLLM Offline Batched Inference

python

from vllm import LLM, SamplingParams

sampling_params = SamplingParams(temperature=0.01, max_tokens=1024, stop=["</s>"])
llm = LLM(model="openGPT-X/Teuken-7B-instruct-v0.6", trust_remote_code=True, dtype="bfloat16") 
outputs = llm.chat(
    messages=[{"role": "User", "content": "Hallo"}], 
    sampling_params=sampling_params, 
    chat_template="DE"
)
print(f"Prompt: {outputs[0].prompt}")
print(f"Assistant: {outputs[0].outputs[0].text}")

Training Details

Pre-Training Data

Teuken-7B-base-v0.6 was pre-trained on 6 trillion tokens of data from publicly available sources. The pretraining data has a cutoff of September 2023. More information is available in our preprint "Data Processing for the OpenGPT-X Model Family".

Training Procedure

Instruction fined tuned version of Teuken-7B-base-v0.6. More information regarding the pre-training are available in our model preprint "Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs".

Training Hyperparameters

Training regime: bf16 mixed precision

Evaluation

Results on multilingual benchmarks for 21 European languages with instruction-tuned models

Model	Avg.	EU21-ARC	EU21-HeSw	EU21-TQA	EU21-MMLU
Meta-Llama-3.1-8B-Instruct	.563	.563	.579	.532	.576
Mistral-7B-Instruct-v0.3	.527	.530	.538	.548	.491
Salamandra-7B-Instruct	.543	.595	.637	.482	.459
Aya-23-8B	.485	.475	.535	.476	.455
Occiglot-7B-eu5-Instruct	.475	.484	.519	.471	.428
Pharia-1-LLM-7B-C-A	.417	.396	.438	.469	.366
Bloomz-7B1	.358	.316	.354	.461	.302
Teuken-7B-instruct-commercial-v0.4	.531	.569	.620	.503	.430
Teuken 7B-instruct-v0.6	.57	.59	.66	.57	.45

More information regarding the quality of our translated benchmarks are available in our Evaluation preprint "Towards Multilingual LLM Evaluation for European Languages". More evaluation results regarding Teuken-7B-instruct-research-v0.4 are available in our model preprint "Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs". The model was evaluated in 21 languages on ARC, GSM8K, HellaSwag, TruthfulQA, Translation and MMLU. Results can also be seen in the European LLM Leaderboard.

Technical Specifications

Model Architecture and Objective

Hyper-Parameter	Value
Training Objective	CLM
Activation Function	SwiGLU
Seq Length	4096
Position Embeddings	Rotary
Num Layers	32
Hidden Size	4096
FFN Hidden Size	13440
Num Attention Heads	32
Head Dim	128
Group Query Attention	yes
Num Query Groups	2
Normalization	RMSNorm
Learning rate	3e-4
Min learning rate	1.5e-5
Disable bias in linear	yes
Hidden dropout	0.0
Attention dropout	0.0
Optimizer	AdamW
Beta1	0.9
Beta2	0.95
Data-type	bf16
Recompute-activations	yes
Distributed-optimizers	yes

Compute Infrastructure

We trained our models on JUWELS Booster which consists of 936 compute nodes, each equipped with 4 NVIDIA A100 GPUs. The GPUs are hosted by AMD EPYC Rome CPUs. The compute nodes are connected with HDR-200 InfiniBand in a DragonFly+ topology.

Hardware

The configuration of JUWELS Booster compute nodes is the following: CPU: AMD EPYC 7402 processor; 2 sockets, 24 cores per socket, SMT-2 (total: 2×24×2 = 96 threads) in NPS-4 1 configuration Memory: 512 GB DDR4-3200 RAM (of which at least 20 GB is taken by the system software stack, including the file system); 256 GB per socket; 8 memory channels per socket (2 channels per NUMA domain) GPU: 4 × NVIDIA A100 Tensor Core GPU with 40 GB; connected via NVLink3 to each other Network: 4 × Mellanox HDR200 InfiniBand ConnectX 6 (200 Gbit/s each), HCA Periphery: CPU, GPU, and network adapter are connected via 2 PCIe Gen 4 switches with 16 PCIe lanes going to each device (CPU socket: 2×16 lanes). PCIe switches are configured in synthetic mode.

Software

Megatron-LM BibTeX: If you find our model useful in your research, please consider citing our preprint:

@misc{ali2024teuken7bbaseteuken7binstructeuropean,
      title={Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs}, 
      author={Mehdi Ali and Michael Fromm and Klaudia Thellmann and Jan Ebert and Alexander Arno Weber and Richard Rutmann and Charvi Jain and Max Lübbering and Daniel Steinigen and Johannes Leveling and Katrin Klug and Jasper Schulze Buschhoff and Lena Jurkschat and Hammam Abdelwahab and Benny Jörg Stein and Karl-Heinz Sylla and Pavel Denisov and Nicolo' Brandizzi and Qasid Saleem and Anirban Bhowmick and Lennard Helmer and Chelsea John and Pedro Ortiz Suarez and Malte Ostendorff and Alex Jude and Lalith Manjunath and Samuel Weinbach and Carolin Penke and Oleg Filatov and Shima Asaadi and Fabio Barth and Rafet Sifa and Fabian Küch and Andreas Herten and René Jäkel and Georg Rehm and Stefan Kesselheim and Joachim Köhler and Nicolas Flores-Herr},
      year={2024},
      eprint={2410.03730},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.03730}, 
}

Team

Data Team

Anirban Bhowmick (IAIS), Nicolo Brandizzi (IAIS), Lennard Helmer (IAIS), Benny Jörg Stein (IAIS), Karl-Heinz Sylla (IAIS), Pavel Denisov (IAIS), Qasid Saleem (IAIS), Johannes Leveling (IAIS), Hammam Abdelwahab (IAIS), Luzian Hahn (IIS), Farzad Naderi (IIS), Md Saiful Islam (IIS), Alexander Schwirjow (IIS), Pedro Ortiz Suarez (ex. DFKI), Malte Ostendorff (ex. DFKI)

Model-Training Team

Core contributors

Mehdi Ali (IAIS), Michael Fromm (IAIS), Jan Ebert (FZJ), Chelsea John (FZJ), Lena Jurkschat (TUD), Alexander Weber (IAIS), Paramita Mirza (IIS), Lucas Weber (IIS)

Contributors:

Richard Rutmann (IAIS), Daniel Steinigen (IAIS), Lalith Manjunath (TUD), Carolin Penke (FZJ)

Evaluation Team

Core contributors

Klaudia Thellmann (TUD), Alex Jude (IAIS), Jasper Buschhoff (IAIS)

Contributors:

Shima Assadi (IIS), Fabio Barth (DFKI)

Management

Joachim Köhler (IAIS), Nicolas Flores-Herr (IAIS), Stefan Kesselheim (FZJ), Andreas Herten (FZJ), Georg Rehm (DFKI), René Jäkel (TUD), Fabian Küch (IIS), Nicole Hildebrandt (IAIS), Ines Wendler (IAIS)

We believe that collaboration is key to overcome the aforementioned limitations and thereby strengthening the European GenAI landscape. Because of this, the team invites researchers, developers, and AI enthusiasts to join and engage through various platforms. A Discord server has been created for community collaboration, offering a space for discussions on technical details, ideas, and direct interaction with developers. Additionally, resources like research publications and a European LLM Leaderboard provide insights into Teuken-7B’s performance and technical aspects. The OpenGPT-X team encourages ongoing engagement and collaboration as the project evolves. Key links: Discord: OpenGPT-X Discord server Research Papers: OpenGPT-X News Research Papers LLM Leaderboard: European LLM Leaderboard LLM Leaderboard

Contact Information

You can reach out to the following model card contact:

OpenGPT-X - contact@opengpt-x.de