62 lines
2.7 KiB
Markdown
62 lines
2.7 KiB
Markdown
---
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datasets:
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- oscar-corpus/OSCAR-2301
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- wikipedia
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- bjoernp/tagesschau-2018-2023
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language:
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- en
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- de
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library_name: transformers
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
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Meet LeoLM-Mistral, the first open and commercially available German Foundation Language Model built on Mistral 7b.
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Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
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Thanks to a compute grant at HessianAI's new supercomputer **42**, we release three foundation models trained with 8k context length.
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[`LeoLM/leo-mistral-hessianai-7b`](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) under Apache 2.0 and
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[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
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With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
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Read our [blog post](https://laion.ai/blog/leo-lm/) or our paper (preprint coming soon) for more details!
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*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
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## Model Details
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- **Finetuned from:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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- **Model type:** Causal decoder-only transformer language model
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- **Language:** English and German
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
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- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
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## Use in 🤗Transformers
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First install direct dependencies:
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```
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pip install transformers torch accelerate
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```
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If you want faster inference using flash-attention2, you need to install these dependencies:
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```bash
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pip install packaging ninja
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pip install flash-attn
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```
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Then load the model in transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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model="LeoLM/leo-mistral-hessianai-7b",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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use_flash_attn_2=True # optional
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)
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```
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## Training parameters
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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.
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## Benchmarks
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