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: apache-2.0
+---
+# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
+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`](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b),
+[`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! 👀).
+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:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** English and German
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
+- **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 accelerate
+```
+If you want faster inference using flash-attention2, you need to install these dependencies:
+```bash
+pip install packaging ninja
+pip install flash-attn
+```
+Then load the model in transformers:
+```python
+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.
+![training_parameters](imgs/training_params.png "Training Hyperparameters")
+
+
+## Benchmarks
+![benchmarks](imgs/benchmarks.png "Benchmark Scores")