metadata

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

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

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

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

Bias, Risks, and 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:

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>"

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

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:

Add all multi-turn examples
Add the entire code_alpaca dataset subset
Add entire lmsys_chat_1m_high_quality_train_en dataset subset
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

Instruction fined tuned version of Teuken-7B-base-v0.4.

Training Hyperparameters

Training regime: bf16 mixed precision

Evaluation

Testing Data, Factors & Metrics

Testing Data

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:

TODO

APA:

TODO

Model Card Contact

Contact Information

You can reach out to the following model card contact:

OpenGPT-X - [email protected]