Instructions to use occiglot/occiglot-7b-de-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use occiglot/occiglot-7b-de-en with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="occiglot/occiglot-7b-de-en")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-de-en")
model = AutoModelForCausalLM.from_pretrained("occiglot/occiglot-7b-de-en", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use occiglot/occiglot-7b-de-en with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "occiglot/occiglot-7b-de-en"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "occiglot/occiglot-7b-de-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/occiglot/occiglot-7b-de-en

SGLang

How to use occiglot/occiglot-7b-de-en with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "occiglot/occiglot-7b-de-en" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "occiglot/occiglot-7b-de-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "occiglot/occiglot-7b-de-en" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "occiglot/occiglot-7b-de-en",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use occiglot/occiglot-7b-de-en with Docker Model Runner:
```
docker model run hf.co/occiglot/occiglot-7b-de-en
```

occiglot-7b-de-en / README.md

mbrack

barthfab

clean table (#5)

93c0cd0 verified over 2 years ago

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	---
	license: apache-2.0
	language:
	- en
	- de
	pipeline_tag: text-generation
	---

	![image/png](https://huggingface.co/datasets/malteos/images/resolve/main/occiglot.medium.png)

	# Occiglot-7B-DE-EN

	> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
	>

	Occiglot-7B-DE-EN is a generative language model with 7B parameters for German and English and trained by the [Occiglot Research Collective](https://occiglot.github.io/occiglot/).
	It is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and trained on 114B tokens of additional multilingual and code data with a block size of 8,192 tokens per sample.
	Note that the model is a general-purpose base model and was not instruction-fine-tuned nor optimized for chat or other applications. We make an instruction tuned variant available as [occiglot-7b-de-en-instruct](https://huggingface.co/occiglot/occiglot-7b-de-en-instruct)

	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](https://discord.gg/wUpvYs4XvM). We are open for collaborations!


	### Model details

	- Continued-pretraining from: [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
	- Model type: Causal decoder-only transformer language model
	- Languages: English, German, and code.
	- License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
	- Compute resources: [HessianAI's 42](https://hessian.ai/)
	- Contributors: Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
	- Research labs: [Occiglot](https://occiglot.github.io/occiglot/) with support from [SAINT](https://www.dfki.de/en/web/research/research-departments/foundations-of-systems-ai) and [SLT](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology)
	- Contact: [Discord](https://discord.gg/wUpvYs4XvM)

	### How to use

	You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
	set a seed for reproducibility:

	```python
	>>> from transformers import pipeline, set_seed
	>>> generator = pipeline('text-generation', model='occiglot/occiglot-7b-de-en')
	>>> set_seed(42)
	>>> generator("Hallo, Ich bin ein Sprachmodell,", max_length=40, num_return_sequences=1)
	[{'generated_text': 'Hallo, Ich bin ein Sprachmodell, das dir bei der Übersetzung von Texten zwischen Deutsch und Englisch helfen kann. Wenn du mir einen Text in Deutsch'}]
	```

	## Dataset

	The training data is the respective subset of the data used for [occiglot-7b-eu5](https://huggingface.co/occiglot/occiglot-7b-eu5), i.e. German plus English and Code.

	The data distribution by language (estimated) is as follows:
	- English: ~34%
	- Code: ~13%
	- German: ~52%

	The training data was prepared using [lm-datasets](https://github.com/malteos/lm-datasets).
	The exact data configuration is [here](https://huggingface.co/occiglot/occiglot-7b-eu5/blob/main/lm-datasets-config.yml).

	## Training settings

	- Continual pre-training on 128 x A100-80GB on [HessianAI's 42](https://hessian.ai/).
	- Framework: [Determined](https://www.determined.ai/)
	- Precision: bf16
	- Optimizer: AdamW (lr: 0.00001, warmup_steps: 420)
	- Global batch size: 512 (with 8192 blocksize) split over 128 GPUs
	- Cosine Annealing with Warmup


	## Tokenizer

	Tokenizer is unchanged from [Mistral-7B-v0.1](https://huggingface.co/mistralai/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](https://huggingface.co/datasets/facebook/belebele) and [Okapi framework](https://github.com/nlp-uoregon/Okapi)) 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.

	<details>
	<summary>Evaluation results</summary>

	### 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 \|


	</details>

	## Acknowledgements

	The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/) which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)).
	The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)
	through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).


	## License

	[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)

	## See also

	- https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01