AfriGuardPlain-tiny-aya-earth-full
CohereLabs/tiny-aya-earth fine-tuned with full fine-tuning (DeepSpeed ZeRO-3) on adzcai/AfriGuard-plain, the non-instruction-prompted version of israel/AfriGuard-inst: each example is only the user prompt and a plain reply, with no safety system instruction and no <safety> / <category> / <response> tags.
Its instruction-prompted counterpart (same base and config) is adzcai/AfriGuardInst-tiny-aya-earth-full.
Training config: examples/train_full/afriguard_plain_tiny-aya-earth_full_sft.yaml in the AfriGuard-model (LlamaFactory) repo, the tiny-aya-global setup (afriguard_tinyaya_full_sft.yaml) with only
the base model and output paths changed; 1 epoch, 1 GPU, cohere chat template, loss on the response only.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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
- Transformers 5.8.0
- Pytorch 2.14.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2