LAION LeoLM: Linguistically Enhanced Open Language Model
Meet LeoLM-Mistral, the first open and commercially available German Foundation Language Model built on Mistral 7b.
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 three foundation models trained with 8k context length.
LeoLM/leo-mistral-hessianai-7b under Apache 2.0 and
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: mistralai/Mistral-7B-v0.1
- Model type: Causal decoder-only transformer language model
- Language: English and German
- License: Apache 2.0
- Contact: LAION Discord or Björn Plüster
Use in 🤗Transformers
First install direct dependencies:
pip install transformers torch accelerate
If you want faster inference using flash-attention2, you need to install these dependencies:
pip install packaging ninja
pip install flash-attn
Then load the model in transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
model="LeoLM/leo-mistral-hessianai-7b",
device_map="auto",
torch_dtype=torch.bfloat16,
use_flash_attn_2=True # optional
)
Training parameters
Note that for Mistral training, we changed learning rate to 1e-5 going down to 1e-6. We also used Zero stage 3 and bfloat16 dtype.
Benchmarks
