petershaan12/qwen2.5-1.5b-alpaca-id-sft
The petershaan12/qwen2.5-1.5b-alpaca-id-sft model is a 1.5 billion parameter Qwen2.5 language model developed by petershaan12, fine-tuned from unsloth/Qwen2.5-1.5B-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for general language tasks, leveraging its Qwen2.5 architecture and 32768 token context length.
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Model Overview
petershaan12/qwen2.5-1.5b-alpaca-id-sft is a 1.5 billion parameter language model based on the Qwen2.5 architecture. Developed by petershaan12, this model was fine-tuned from the unsloth/Qwen2.5-1.5B-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen2.5, a powerful transformer-based language model.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining coherence over extended conversations or documents.
- Training Methodology: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process. This approach often leads to efficient and well-optimized models.
Use Cases
This model is suitable for a variety of general language understanding and generation tasks, particularly where the Qwen2.5 architecture's strengths are beneficial. Its efficient fine-tuning process suggests it could be a good candidate for applications requiring custom adaptations without extensive computational resources.