Quantization made by Richard Erkhov.
llama2-13b-ft-mc4_nl_cleaned_tiny - GGUF
- Model creator: https://huggingface.co/BramVanroy/
- Original model: https://huggingface.co/BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny/
Original model description:
license: apache-2.0 base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer - llama - lora - adapters datasets: - yhavinga/mc4_nl_cleaned language: - nl model-index: - name: llama2-13b-ft-mc4_nl_cleaned_tiny results: []
llama2-13b-ft-mc4_nl_cleaned_tiny
This model is a fine-tuned version of meta-llama/Llama-2-13b-hf
on the yhavinga/mc4_nl_cleaned dataset (tiny partition) on a context of 4096 tokens.
See the original meta-llama/Llama-2-13b-hf for more information, intended use, and biases.
If you use this model or refer to it, please use the following citation:
Vanroy, B. (2023). Language Resources for Dutch Large Language Modelling. https://arxiv.org/abs/2312.12852
@article{vanroy2023language,
title={Language Resources for {Dutch} Large Language Modelling},
author={Vanroy, Bram},
journal={arXiv preprint arXiv:2312.12852},
year={2023}
}
Intended uses & limitations
While Llama 2 already contains some proficiency in Dutch, this finetune is intended to improve the fluency of Dutch (not increase its knowledge). It is therefore intended as a generative model for Dutch language. The biases, shortcomings and intended uses are otherwise the same as those of the original model. The model can be used for generative tasks or finetuned further on other tasks such as summarization, adaptation, instruction or chat finetuning.
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.
Training procedure
Trained with LoRA targetting ["q_proj", "v_proj"] in 4 bit and merged before upload. Trained with Flash Attention as borrowed from
here.
The adapters are in the adapters branch.
Initial training investigation on the Tier-1 HPC of Vlaams Supercomputer Centrum (VSC) and training on our own server of 4x 3090s.
Training hyperparameters
The following hyperparameters were used during training in the HPC investigation:
- learning_rate: 0.0003
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 6
- total_train_batch_size: 1152
- total_eval_batch_size: 192
- 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 |
|---|---|---|---|
| 1.8784 | 0.09 | 90 | 1.8820 |
| 1.8344 | 0.19 | 180 | 1.8542 |
| 1.8351 | 0.28 | 270 | 1.8355 |
| 1.8206 | 0.37 | 360 | 1.8212 |
| 1.8021 | 0.47 | 450 | 1.8088 |
| 1.8102 | 0.56 | 540 | 1.7982 |
| 1.7991 | 0.65 | 630 | 1.7890 |
| 1.7788 | 0.74 | 720 | 1.7811 |
| 1.7915 | 0.84 | 810 | 1.7742 |
| 1.7715 | 0.93 | 900 | 1.7676 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 46.81 |
| ARC (25-shot) | 59.3 |
| HellaSwag (10-shot) | 82.04 |
| MMLU (5-shot) | 54.67 |
| TruthfulQA (0-shot) | 38.03 |
| Winogrande (5-shot) | 77.27 |
| GSM8K (5-shot) | 10.31 |
| DROP (3-shot) | 6.08 |
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