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README.md ADDED
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+ ---
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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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+ ---
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+ # LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
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+ Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
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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 two foundation models trained with 8k context length,
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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]() or our paper (preprint coming soon) for more details!
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+
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+ *A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
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+
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+
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+ ## Model Details
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+ - **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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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:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
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+ - **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
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+
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+
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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 sentencepiece
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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==v2.1.1 --no-build-isolation
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+ pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
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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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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model="LeoLM/leo-hessianai-7b",
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+ device_map="auto",
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+ torch_dtype=torch.float16,
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+ trust_remote_code=True # True for flash-attn2 else False
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+ )
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+ ```
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+
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+ ## Training parameters
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+ ![training_parameters](imgs/training_params.png "Training Hyperparameters")
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+
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+
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+ ## Benchmarks
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+ ![benchmarks](imgs/benchmarks.png "Benchmark Scores")
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+ "LlamaForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 11008,
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+ "max_position_embeddings": 8192,
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+ "model_type": "llama",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "pad_token_id": 0,
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+ "type": "linear"
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.32.0",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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+ "transformers_version": "4.32.0"
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