hausa_xla

Autoregressive Transformer trained on hausa_datamix

This is an autoregressive decoder-only transformer model trained on the hausa_datamix dataset using JAX and Flax NNX.

Model Details

• Model Type: Autoregressive Decoder-only Transformer
• Framework: JAX + Flax NNX
• Dataset: hausa_datamix
• Parameters: ~83.9M
• Precision: Mixed (FP32 parameters, BF16 computation)

Architecture

- Hidden Size: 512
- Number of Layers: 8
- Attention Heads: 8
- Intermediate Size: 2048
- Max Position Embeddings: 256
- Vocab Size: 49152
- Rotary Position Embeddings: True

Training Details

• Training Steps: 3,120
• Batch Size: 32
• Gradient Accumulation: 4
• Learning Rate: 0.0003
• Training Duration: 0.35 hours
• Final Eval Loss: 0.9853516221046448
• Final Eval Perplexity: 2.820033550262451

Usage

# This model was trained with JAX/Flax and requires the custom transformer implementation
# to load and use. See the repository for implementation details.

from transformers import AutoTokenizer
import jax.numpy as jnp

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("thiomajid/hausa_lm")
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Example text generation (requires custom model loading)
prompt = "Once upon a time, there was a little"
# ... (model loading and generation code)

Training Configuration

model:
  hidden_size: 512
  num_layers: 8  
  num_attention_heads: 8
  intermediate_size: 2048
  max_position_embeddings: 256

training:
  learning_rate: 0.0003
  batch_size: 32
  epochs: 10
  warmup_ratio: 0.1

Files

• config.json: Model configuration
• train_history.json: Training metrics and duration
• tokenizer/: hausa_lm tokenizer files
• model_checkpoint/: Best model checkpoint
• tensorboard_logs/: Training logs for TensorBoard

License

MIT License - see LICENSE file for details.