AfriGuardPlain-tiny-aya-global-lora-merged
CohereLabs/tiny-aya-global fine-tuned with LoRA (rank 16, all linear layers), merged into the base weights on adzcai/AfriGuard-plain: the non-instruction-prompted version of israel/AfriGuard-inst.
Each training example is only the user prompt and a plain reply (a helpful answer for safe prompts, a brief
refusal for unsafe ones), with no safety system instruction and no <safety> / <category> / <response>
tags. The model therefore answers or refuses directly, without emitting a safety label.
Training config: examples/train_lora/afriguard_plain_tiny-aya-global_lora_sft.yaml in the AfriGuard-model (LlamaFactory) repo; 1 epoch, 1 GPU, chat template
of the base model, loss on the response only.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.18.1
- Transformers 5.8.0
- Pytorch 2.14.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2