azherali/Riazi-8B
azherali/Riazi-8B is an 8 billion parameter language model developed by azherali, fine-tuned using Supervised Fine-Tuning (SFT). It is designed for general language tasks, demonstrated by its ability to process and respond to prompts in languages like Urdu. The model supports a context length of up to 32768 tokens and can be efficiently run with 4-bit or 8-bit quantization for reduced memory usage.
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Model Overview
azherali/Riazi-8B is an 8 billion parameter language model developed by azherali, fine-tuned using Supervised Fine-Tuning (SFT). It is built to handle a variety of language tasks, as showcased by its ability to process and generate responses for prompts in languages such as Urdu. The model supports a flexible context length, with a default configuration of 2048 tokens, and can be extended up to 32768 tokens through internal RoPE Scaling.
Key Capabilities
- Multilingual Processing: Demonstrated ability to understand and generate text in languages like Urdu.
- Efficient Inference: Supports 4-bit and 8-bit quantization for reduced memory footprint, making it suitable for environments with limited resources.
- Optimized Performance: Leverages
unsloth'sFastLanguageModelfor native 2x faster inference, enhancing generation speed. - Flexible Context Window: Internally supports RoPE Scaling, allowing for context lengths up to 32768 tokens.
Training Details
The model was trained using Supervised Fine-Tuning (SFT). The training procedure utilized several popular frameworks:
- TRL: 0.22.2
- Transformers: 4.56.2
- Pytorch: 2.12.0+rocm7.2
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Good For
- Applications requiring multilingual text generation and understanding, particularly for languages like Urdu.
- Deployment in resource-constrained environments due to support for 4-bit and 8-bit quantization.
- Tasks benefiting from fast inference speeds provided by
unslothoptimizations.