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---
license: apache-2.0
base_model: tiiuae/falcon-7b
datasets:
- yhavinga/mc4_nl_cleaned
model-index:
- name: falcon-7b-ft-mc4_nl_cleaned_tiny
results: []
language:
- nl
inference: false
tags:
- falcon
---
# falcon-7b-ft-mc4_nl_cleaned_tiny
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
on the [yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) dataset (`tiny` partition) on a context of 2048 tokens.
See the original [tiiuae/falcon-7b](https://huggingface.co/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](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6094 | 0.1 | 170 | 2.5980 |
| 2.4503 | 0.19 | 340 | 2.4405 |
| 2.3243 | 0.29 | 510 | 2.3428 |
| 2.2822 | 0.39 | 680 | 2.2752 |
| 2.238 | 0.49 | 850 | 2.2248 |
| 2.2015 | 0.58 | 1020 | 2.1865 |
| 2.1678 | 0.68 | 1190 | 2.1560 |
| 2.1301 | 0.78 | 1360 | 2.1312 |
| 2.1161 | 0.88 | 1530 | 2.1112 |
| 2.0997 | 0.97 | 1700 | 2.0928 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3 |