AfriGuardInst-AfriqueQwen3.5-4B
McGill-NLP/AfriqueQwen3.5-4B fine-tuned with full fine-tuning (DeepSpeed ZeRO-3) on
israel/AfriGuard-inst: each example is one of the
safety system instructions plus the user prompt, and the target is
<safety>…</safety> <category>…</category> <response>…</response>.
This is a rerun of the AfriGuard-inst config afriguard2.yaml from the AfriGuard-model (LlamaFactory) repo, with only
the output paths changed (examples/train_full/afriguard_inst_afriqueqwen3.5-4b_full_sft.yaml), made as the matched baseline for
adzcai/AfriGuardPlain-AfriqueQwen3.5-4B, the same setup trained on
adzcai/AfriGuard-plain (no instruction, no tags).
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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