UdayGattu23/qwen2.5-7b-finetuned-argus
UdayGattu23/qwen2.5-7b-finetuned-argus is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-7B using LoRA on a dataset of 27,997 text samples. This model leverages a 32K context window and is optimized for general text generation tasks, offering various GGUF quantization levels for flexible deployment. Its primary use case is to provide a specialized version of Qwen2.5-7B with potentially improved performance on tasks aligned with its training data.
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Overview
UdayGattu23/qwen2.5-7b-finetuned-argus is a 7.6 billion parameter language model derived from the Qwen/Qwen2.5-7B base model. It has been fine-tuned using the LoRA (Low-Rank Adaptation) method with a rank of 128 and an alpha of 256. The training utilized a dataset comprising 27,997 text samples over two epochs, with the best performance observed at epoch 1.
Key Capabilities
- Specialized Fine-tuning: Built upon the robust Qwen2.5-7B architecture, this model is further adapted through LoRA on a specific dataset, potentially enhancing its performance for tasks related to the training data.
- Flexible Deployment: Available in multiple GGUF quantization levels (Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0), allowing users to balance model quality and file size for various hardware constraints.
- Standard Integration: Compatible with popular libraries like Hugging Face Transformers and
llama.cppfor easy integration into existing workflows.
Good for
- Developers seeking a fine-tuned variant of Qwen2.5-7B for general text generation.
- Applications requiring a balance of model size and performance, leveraging GGUF quantizations.
- Experimentation with LoRA-tuned models based on established architectures.