---
language:
- de
- bg
- cs
- da
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sl
- sv
- sk
metrics:
- accuracy
- bleu
pipeline_tag: text-generation
library_name: transformers
base_model:
- openGPT-X/Teuken-7B-base-v0.4
license: apache-2.0
---
# Model Card for Teuken-7B-instruct-v0.4

Teuken-7B-instruct-v0.4 is an instruction-tuned version of Teuken-7B-base-v0.4.


### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Fraunhofer IAIS
- **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:** Fraunhofer IAIS

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Teuken-7B-instruct-v0.4 is intended for commercial and research use in all official 24 European languages. Since Teuken-7B-chat-v0.4 focuses on covering all 24 EU languages, it renders more stable results across these languages and better reflects European values in its answers than English-centric models. It is therefore specialized for use in multilingual tasks.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Teuken-7B-instruct-v0.4 is an instruction-tuned version of Teuken-7B-base-v0.4 that is not completely free from biases and hallucinations.

## How to Get Started with the Model

## Usage
The model requires transformers, sentencepiece, and the torch library.
After installation, here's an example of how to use the model:

The prompt template for the fine-tuned model 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:<s>"
```

```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-v0.4"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)
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])
print(prediction_text)
```

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

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

For composing the final instruction-tuning dataset termed "Honey", we first include all German examples. We aim to include roughly the same amount of English examples, as we have German examples:
  1. Add all multi-turn examples
  2. Add the entire code_alpaca dataset subset
  3. Add entire lmsys_chat_1m_high_quality_train_en dataset subset
  4. For the remaining dataset subsets ("open_orca", "evol_instruct_143k", "evol_instruct_70k", "bactrianx_EN") add the examples with the highest reward scores ("quality score") so that each dataset subset contributes an equal amount of high-quality examples

## Dataset Sizes Before Composition

### English



### German



### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Instruction fined tuned version of Teuken-7B-base-v0.4.


#### Training Hyperparameters

- **Training regime:** bf16 mixed precision <!--fp32, fp16 mixed precision, , bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

The model was evaluated in 21 languages on ARC, GSM8K, HellaSwag, TruthfulQA, Translation and MMLU. Results can be seen in the European LLM Leaderboard (https://huggingface.co/spaces/openGPT-X/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          | 3e-5     |
| Disable bias in linear     | yes      |
| Hidden dropout             | 0.0      |
| Attention dropout          | 0.0      |
| Optimizer                  | AdamW    |
| Beta1                      | 0.9      |
| Beta2                      | 0.95     |
| Sequence-parallelism      
| Data-type                  | bf16     |
| Recompute-activations      | yes      |
| Distributed-optimizers     | yes      |
| Model Initialization       |          |



**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)
### 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)

## Model Card Contact

<div class="hf-card">
    <h2>Contact Information</h2>
    <p>You can reach out to the following model card contact:</p>
    <ul>
        <li>
            <a href="https://huggingface.co/openGPT-X" target="_blank">OpenGPT-X</a> 
            - <a href="contact@opengpt-x.de">contact@opengpt-x.de</a>
        </li>
    </ul>
</div>