Llama 3.1 8B Danoia

This model is a fine-tuned version of 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.

It achieves the following results on the evaluation set:

• Loss: 0.7108

Model description

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.

Intended uses & limitations

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.

Training and evaluation data

I trained this using LLama-Factory with unsloth enabled on a 16GB 4060 Ti. It took 30 hours and peaked at 13GB VRAM usage.

### model
model_name_or_path: unsloth/Meta-Llama-3.1-8B-Instruct

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
loraplus_lr_ratio: 16.0
lora_rank: 16
lora_alpha: 32
use_unsloth: true
use_unsloth_gc: true
quantization_bit: 4
upcast_layernorm: true
seed: 192

### dataset
dataset: danoia_v03,opus_writing_instruct,creativegpt,danoia_v02_no_system
template: llama3
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: llama31/8b_instruct/loras/danoia
logging_steps: 1
save_steps: 500
save_strategy: steps
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 1.5e-5
num_train_epochs: 1.5
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true

## eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1.5e-05
• train_batch_size: 2
• eval_batch_size: 1
• seed: 192
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 1.5

Training results

Framework versions

• PEFT 0.12.0
• Transformers 4.46.1
• Pytorch 2.5.1
• Datasets 3.1.0
• Tokenizers 0.20.3