Overview

The cxrbon16/turkish-llama-MSFT-0.7 is an 8 billion parameter language model, fine-tuned from the ytu-ce-cosmos/Turkish-Llama-8b-v0.1 base model. This adaptation focuses on enhancing its capabilities for the Turkish language, making it a specialized tool for Turkish NLP applications. The model was trained with a context length of 8192 tokens.

Training Details

The fine-tuning process involved specific hyperparameters:

• Learning Rate: 2e-05
• Batch Sizes: train_batch_size of 2, eval_batch_size of 8, and a total_train_batch_size of 32 (with gradient_accumulation_steps of 16).
• Optimizer: ADAMW_TORCH_FUSED with default betas and epsilon.
• Epochs: 2 training epochs.

During training, the model achieved a final validation loss of 0.3326, indicating its performance on the evaluation set. The training loss progressively decreased from 0.4866 to 0.2526 over 360 steps.

Key Characteristics

• Turkish Language Focus: Specialized fine-tuning for Turkish NLP tasks.
• Llama Architecture: Built upon the robust Llama model family.
• Parameter Count: 8 billion parameters, offering a balance of capability and efficiency.
• Context Length: Supports an 8192-token context window.

Potential Use Cases

This model is suitable for applications requiring strong Turkish language understanding and generation, such as:

• Turkish text summarization.
• Turkish question answering systems.
• Content generation in Turkish.
• Language understanding tasks specific to Turkish.