Occiglot 7b De En Instruct

Developed by occiglot

Occiglot-7B-DE-EN-Instruct is a multilingual large language model supporting German and English, developed based on occiglot-7b-eu5 with instruction fine-tuning, containing 7 billion parameters.

Large Language Model

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #German-English bilingual #Instruction fine-tuning #Not safety-aligned

Downloads 317

Release Time : 3/5/2024

Model Overview

This model is a multilingual large language model for the Western world, supporting German and English as well as code generation, trained by the Occiglot research community. The model was additionally trained with 180 million multilingual and code instruction tokens but has not undergone safety alignment, potentially generating problematic outputs.

Model Features

Multilingual support

Supports German and English, as well as code generation, suitable for multilingual environments.

Instruction fine-tuning

Developed based on occiglot-7b-eu5 with instruction fine-tuning, suitable for dialogue and instruction response scenarios.

High-performance training

Trained with 180 million multilingual and code instruction tokens, optimizing generation capabilities.

Model Capabilities

Text generation

Multilingual dialogue

Code generation

Use Cases

Dialogue systems

Multilingual customer service

Used for German and English customer service dialogue systems, providing concise responses.

Capable of generating contextually appropriate responses, such as answering who the German Chancellor is.

Code assistance

Code generation

Supports generation and completion of code snippets, suitable for developers.

license: apache-2.0 language:

en
de pipeline_tag: text-generation

image/png

Occiglot-7B-DE-EN-Instruct

A polyglot language model for the Occident.

Occiglot-7B-DE-EN-Instruct is a the instruct version of occiglot-7b-eu5, a generative language model with 7B parameters supporting German and English and trained by the Occiglot Research Collective. It was trained on 180M tokens of additional multilingual and code instructions. Note that the model was not safety aligned and might generate problematic outputs.

This is the first release of an ongoing open research project for multilingual language models. If you want to train a model for your own language or are working on evaluations, please contact us or join our Discord server. We are open for collaborations!

Special thanks go to Disco Research, Jan Philipp Harries, and Björn Plüster for sharing the German dataset with us

Model details

Instruction tuned from: occiglot-7b-de-en
Model type: Causal decoder-only transformer language model
Languages: English, German, and code.
License: Apache 2.0
Compute resources: DFKI cluster
Contributors: Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
Research labs: Occiglot with support from SAINT and SLT
Contact: Discord

How to use

The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction. Since the generation relies on some randomness, we set a seed for reproducibility:

>>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
>>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-de-en-instruct")
>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-de-en-instruct')  # You may want to use bfloat16 and/or move to GPU here
>>> set_seed(42)
>>> messages = [
>>>    {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
>>>    {"role": "user", "content": "Wer ist der deutsche Bundeskanzler?"},
>>> ]
>>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
>>> set_seed(42)
>>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
>>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
'Der deutsche Bundeskanzler ist Olaf Scholz.'

Dataset

The training data was split evenly between German and English based on the total number of tokens. We would like to thank Disco Research, Jan Philipp Harries, and Björn Plüster for making their dataset available to us.

English and Code

Open-Hermes-2B

German

DiscoLM German Dataset includes the publicly available germanrag dataset
OASST-2 (German subset)
Aya-Dataset (German subset)

Training settings

Full instruction fine-tuning on 8xH100.
0.6 - 4 training epochs (depending on dataset sampling).
Framework: axolotl
Precision: bf16
Optimizer: AdamW
Global batch size: 128 (with 8192 context length)
Cosine Annealing with Warmup

Tokenizer

Tokenizer is unchanged from Mistral-7B-v0.1.

Evaluation

Preliminary evaluation results can be found below. Please note that the non-English results are based on partially machine-translated datasets and English prompts (Belebele and Okapi framework) and thus should be interpreted with caution, e.g., biased towards English model performance. Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.

Evaluation results

All 5 Languages

	avg	arc_challenge	belebele	hellaswag	mmlu	truthfulqa
Occiglot-7b-eu5	0.516895	0.508109	0.675556	0.718963	0.402064	0.279782
Occiglot-7b-eu5-instruct	0.537799	0.53632	0.691111	0.731918	0.405198	0.32445
Occiglot-7b-de-en	0.518337	0.496297	0.715111	0.669034	0.412545	0.298697
Occiglot-7b-de-en-instruct	0.543173	0.530826	0.745778	0.67676	0.411326	0.351176
Leo-mistral-hessianai-7b	0.484806	0.462103	0.653556	0.642242	0.379208	0.28692
Mistral-7b-v0.1	0.547111	0.528937	0.768444	0.682516	0.448253	0.307403
Mistral-7b-instruct-v0.2	0.56713	0.547228	0.741111	0.69455	0.422501	0.430262

English

	avg	arc_challenge	belebele	hellaswag	mmlu	truthfulqa
Occiglot-7b-eu5	0.59657	0.530717	0.726667	0.789882	0.531904	0.403678
Occiglot-7b-eu5-instruct	0.617905	0.558874	0.746667	0.799841	0.535109	0.449
Occiglot-7b-de-en	0.518337	0.496297	0.715111	0.669034	0.412545	0.298697
Occiglot-7b-de-en-instruct	0.543173	0.530826	0.745778	0.67676	0.411326	0.351176
Leo-mistral-hessianai-7b	0.600949	0.522184	0.736667	0.777833	0.538812	0.429248
Mistral-7b-v0.1	0.668385	0.612628	0.844444	0.834097	0.624555	0.426201
Mistral-7b-instruct-v0.2	0.713657	0.637372	0.824444	0.846345	0.59201	0.668116

German

	avg	arc_challenge_de	belebele_de	hellaswag_de	mmlu_de	truthfulqa_de
Occiglot-7b-eu5	0.508311	0.493584	0.646667	0.666631	0.483406	0.251269
Occiglot-7b-eu5-instruct	0.531506	0.529512	0.667778	0.685205	0.488234	0.286802
Occiglot-7b-de-en	0.540085	0.50556	0.743333	0.67421	0.514633	0.26269
Occiglot-7b-de-en-instruct	0.566474	0.54491	0.772222	0.688407	0.515915	0.310914
Leo-mistral-hessianai-7b	0.517766	0.474765	0.691111	0.682109	0.488309	0.252538
Mistral-7b-v0.1	0.527957	0.476476	0.738889	0.610589	0.529567	0.284264
Mistral-7b-instruct-v0.2	0.535215	0.485885	0.688889	0.622438	0.501961	0.376904

Acknowledgements

The pre-trained model training was supported by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)). The curation of the training data is partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).

License

Apache 2.0