metadata

datasets:
  - oscar-corpus/OSCAR-2301
  - wikipedia
  - bjoernp/tagesschau-2018-2023
language:
  - en
  - de
library_name: transformers
pipeline_tag: text-generation

LAION LeoLM: Linguistically Enhanced Open Language Model

Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, LeoLM/leo-hessianai-7b and LeoLM/leo-hessianai-13b under the Llama-2 community license (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our blog post or our paper (preprint coming soon) for more details!

A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.

Model Details

Finetuned from: meta-llama/Llama-2-13b-hf
Model type: Causal decoder-only transformer language model
Language: English and German
License: LLAMA 2 COMMUNITY LICENSE AGREEMENT
Contact: LAION Discord or Björn Plüster

Use in 🤗Transformers

First install direct dependencies:

pip install transformers torch sentencepiece

If you want faster inference using flash-attention2, you need to install these dependencies:

pip install packaging ninja
pip install flash-attn==v2.1.1 --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary

Then load the model in transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    model="LeoLM/leo-hessianai-13b",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True  # True for flash-attn2 else False
)

LeoLM
/

leo-hessianai-13b

LAION LeoLM: Linguistically Enhanced Open Language Model

Model Details

Use in 🤗Transformers

Training parameters

Benchmarks