falcon-7b-ft-mc4_nl_cleaned_tiny

falcon-7b-ft-mc4_nl_cleaned_tiny

This model is a fine-tuned version of tiiuae/falcon-7b on the yhavinga/mc4_nl_cleaned dataset (tiny partition) on a context of 2048 tokens. See the original tiiuae/falcon-7b for more information, intended use, and biases.

Intended uses & limitations

This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes.

Importantly, the original Falcon 7B model was only trained on English and French. Therefore, Dutch generations should be taken with a massive grain of salt. I wanted to see if the performance would be reasonable after finetuning this model on a Dutch dataset. I find that it is okay but not great. It's especially not coherent.

Training and evaluation data

Trained on the yhavinga/mc4_nl_cleaned dataset (tiny partition) for one epoch. The canonical validation split was not used but instead 5% of train was used as validation.

At 2048 tokens context length, the training set was around 2M (2,008,858) samples, and the model was trained for 1 epoch. That means that the model was trained for around 4B Dutch tokens (2048 * 2008858 = 4.114.141.184).

Training procedure

Trained with LoRA targetting ['query_key_value', 'dense', 'dense_h_to_4h', 'dense_4h_to_h'] in 4 bit and merged before upload. The adapters are in the adapters branch.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 12
• eval_batch_size: 24
• seed: 42
• distributed_type: multi-GPU
• num_devices: 16
• gradient_accumulation_steps: 6
• total_train_batch_size: 1152
• total_eval_batch_size: 384
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 1

Training results

Framework versions

• Transformers 4.31.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3