初始化项目，由ModelHub XC社区提供模型

Model: LeoLM/leo-hessianai-7b
Source: Original Platform

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
+---
+# LAION LeoLM: **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 two foundation models trained with 8k context length,
+[`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]() 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-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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 sentencepiece
+```
+If you want faster inference using flash-attention2, you need to install these dependencies:
+```bash
+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:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    model="LeoLM/leo-hessianai-7b",
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True  # True for flash-attn2 else False
+)
+```
+
+## Training parameters
+![training_parameters](imgs/training_params.png "Training Hyperparameters")
+
+
+## Benchmarks
+![benchmarks](imgs/benchmarks.png "Benchmark Scores")