YoussefKhalaf/Hadith
YoussefKhalaf/Hadith is an 8 billion parameter Qwen3-based causal language model developed by YoussefKhalaf, fine-tuned from Mushari440/Qwen3-8B-SFT-v2. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language generation tasks, leveraging its efficient fine-tuning process.
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Model Overview
YoussefKhalaf/Hadith is an 8 billion parameter language model based on the Qwen3 architecture, developed by YoussefKhalaf. It was fine-tuned from the Mushari440/Qwen3-8B-SFT-v2 model, leveraging efficient training methodologies.
Key Characteristics
- Architecture: Qwen3-based, a powerful causal language model family.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned with Unsloth and Huggingface's TRL library, resulting in a 2x speed improvement during the training process.
- Context Length: Supports a context window of 32768 tokens, enabling processing of longer inputs.
Potential Use Cases
This model is suitable for a variety of natural language processing tasks, particularly those benefiting from its Qwen3 foundation and efficient fine-tuning. Its capabilities make it a strong candidate for:
- General text generation and completion.
- Instruction following and conversational AI.
- Applications requiring a robust 8B parameter model with a large context window.