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--- |
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license: llama3.1 |
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datasets: |
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- trollek/Danoia-v03 |
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- trollek/Danoia-v02 |
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- N8Programs/CreativeGPT |
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- Gryphe/Opus-WritingPrompts |
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language: |
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- da |
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- en |
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base_model: |
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- unsloth/Meta-Llama-3.1-8B-Instruct |
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library_name: transformers |
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tags: |
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- llama-factory |
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- unsloth |
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--- |
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# Llama 3.1 8B Danoia |
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This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the danoia_v03, the opus_writing_instruct, the creativegpt and the danoia_v02_no_system datasets + some private datasets related to evaluation. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7108 |
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## Model description |
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This model can write stories in danish and english. It can do much more, I am sure of it, but not more than the vanilla model it is based on. |
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## Intended uses & limitations |
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Danoia is intended to be private assistant able to write essays, summarise articles, and be a helpful assistant in general. It misspells danish words at times but it is rare though. |
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## Training and evaluation data |
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I trained this using [LLama-Factory](https://github.com/hiyouga/LLaMA-Factory "LLama Factorys' GitHub") with [unsloth](https://github.com/unslothai/unsloth "unsloths' GitHub") enabled on a 16GB 4060 Ti. It took 30 hours and peaked at 13GB VRAM usage. |
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<details> |
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<summary>Show LLama-Factory config</summary> |
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```yaml |
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### model |
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model_name_or_path: unsloth/Meta-Llama-3.1-8B-Instruct |
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### method |
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stage: sft |
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do_train: true |
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finetuning_type: lora |
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lora_target: all |
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loraplus_lr_ratio: 16.0 |
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lora_rank: 16 |
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lora_alpha: 32 |
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use_unsloth: true |
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use_unsloth_gc: true |
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quantization_bit: 4 |
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upcast_layernorm: true |
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seed: 192 |
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### dataset |
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dataset: danoia_v03,opus_writing_instruct,creativegpt,danoia_v02_no_system |
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template: llama3 |
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cutoff_len: 8192 |
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overwrite_cache: false |
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preprocessing_num_workers: 12 |
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### output |
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output_dir: llama31/8b_instruct/loras/danoia |
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logging_steps: 1 |
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save_steps: 500 |
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save_strategy: steps |
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plot_loss: true |
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overwrite_output_dir: false |
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### train |
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per_device_train_batch_size: 2 |
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gradient_accumulation_steps: 4 |
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learning_rate: 1.5e-5 |
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num_train_epochs: 1.5 |
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lr_scheduler_type: cosine |
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warmup_ratio: 0.01 |
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bf16: true |
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## eval |
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val_size: 0.01 |
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per_device_eval_batch_size: 1 |
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eval_strategy: steps |
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eval_steps: 500 |
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``` |
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</details> |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1.5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 192 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 1.5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.2352 | 0.0719 | 500 | 0.8450 | |
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| 0.1742 | 0.1438 | 1000 | 0.8090 | |
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| 0.1667 | 0.2156 | 1500 | 0.7889 | |
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| 0.3791 | 0.2875 | 2000 | 0.7750 | |
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| 0.1989 | 0.3594 | 2500 | 0.7665 | |
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| 0.2347 | 0.4313 | 3000 | 0.7563 | |
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| 0.1694 | 0.5032 | 3500 | 0.7498 | |
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| 0.2351 | 0.5750 | 4000 | 0.7412 | |
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| 0.2322 | 0.6469 | 4500 | 0.7363 | |
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| 0.1689 | 0.7188 | 5000 | 0.7298 | |
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| 0.1953 | 0.7907 | 5500 | 0.7250 | |
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| 0.2099 | 0.8626 | 6000 | 0.7214 | |
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| 0.2368 | 0.9344 | 6500 | 0.7166 | |
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| 0.1632 | 1.0063 | 7000 | 0.7151 | |
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| 0.1558 | 1.0782 | 7500 | 0.7157 | |
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| 0.2854 | 1.1501 | 8000 | 0.7139 | |
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| 0.199 | 1.2220 | 8500 | 0.7127 | |
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| 0.1606 | 1.2938 | 9000 | 0.7117 | |
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| 0.1788 | 1.3657 | 9500 | 0.7112 | |
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| 0.2618 | 1.4376 | 10000 | 0.7109 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.46.1 |
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- Pytorch 2.5.1 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
