Create README.md

README.md

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+---
+datasets:
+- oscar-corpus/OSCAR-2301
+- wikipedia
+- bjoernp/tagesschau-2018-2023
+language:
+- en
+- de
+library_name: transformers
+pipeline_tag: text-generation
+license: llama2
+---
+# LAION LeoLM 70b: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
+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 a series foundation models trained with 8k context length
+under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt). Now, we're finally releasing the
+much anticipated `leo-hessianai-70b`, the largest model of this series based on `Llama-2-70b`.
+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](https://laion.ai/blog/leo-lm/) 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-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** English and German
+- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
+- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
+
+
+## Use in 🤗Transformers
+First install direct dependencies:
+```
+pip install transformers torch
+```
+
+Then load the model in transformers. Note that this requires lots of VRAM and most-likely multiple devices. Use `load_in_8bit=True` or `load_in_4bit=True`
+to save some memory by using a quantized version. For more quantized versions, check out our models at TheBloke's page: (coming soon!)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    model="LeoLM/leo-hessianai-70b",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    use_flash_attention_2=False   # Set to true to use FA2. Requires `pip install flash-attn`
+)
+```
+
+## Training parameters
+![training_parameters](imgs/training_params.png "Training Hyperparameters")
+
+
+## Benchmarks
+![benchmarks](imgs/benchmarks.png "Benchmark Scores")
+![benchmarks](imgs/translation_scores.png "Translation Benchmark Scores")