Geitje 7B Chat

🚀 GEITje-7B-chat

GEITje-7B-chat is a large open Dutch language model. It's based on Mistral 7B and further trained on Dutch text, enhancing its Dutch language skills and knowledge of Dutch topics. However, due to a request, the model is no longer available.

⚠️ Important Note

At the pressing request of Stichting BREIN, GEITje is no longer available, starting immediately. All model files (the weights) and checkpoints have been deleted from this repo. See my blog post (Dutch, English) for further clarification.

🐐 Check out GEITje-7b-chat-v2 🐐

✨ Features

Model Structure

• Base Model - Mistral 7B: GEITje is built upon Mistral 7B, a large open language model with 7 billion parameters trained by Mistral AI. It outperforms Llama 2 13B on English-language benchmarks and is released under the Apache 2.0 open source license.
• Dutch Text Training - GEITje: GEITje is created by further training Mistral 7B on 10 billion tokens of Dutch text from the Dutch Gigacorpus and the MADLAD-400 web crawling corpus. It's a full-parameter finetune with a context length of 8,192 tokens.
• Dialogue Finetuning - GEITje-chat: Two initial chat variants, GEITje-chat and GEITje-chat-v2, are finetuned for chat applications, capable of following instructions, answering questions, and holding dialogues on various topics.

Performance Improvement

The additional training on Dutch text has improved GEITje's Dutch language skills and increased its knowledge of Dutch topics.

📚 Documentation

Model description

Mistral – Base Model

GEITje is based on Mistral 7B. It's a large open language model with 7 billion parameters, trained by Mistral AI. According to Mistral AI, the 7B model performs better than Llama 2 13B on all (English-language) benchmarks they tested it on. Mistral 7B has been released under the Apache 2.0 open source license.

GEITje – Trained Further on Dutch Texts

GEITje was created by further training Mistral 7B on no less than 10 billion tokens of Dutch text from the Dutch Gigacorpus and the MADLAD-400 web crawling corpus. It is a so-called full-parameter finetune: performed on all parameters. It is not a PEFT or LoRA finetune. Like Mistral, GEITje has a context length of 8,192 tokens.

GEITje-chat – Finetuned for Dialogues

As a demonstration of GEITje's capabilities for chat applications, two initial chat variants of GEITje have also been finetuned: GEITje-chat and GEITje-chat-v2. They can follow instructions, answer questions, and hold dialogues on a variety of topics.

More info

Read more about GEITje-chat in the 📄 README on GitHub.

Checkpoints

Intermediate checkpoints are available in the checkpoints branch.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0

📄 License

This project is licensed under the Apache 2.0 license.