Faishal-Anwar/qwen2.5-1.5b-pgabl-legal-sft-faishal
The Faishal-Anwar/qwen2.5-1.5b-pgabl-legal-sft-faishal is a 1.5 billion parameter Qwen2.5 model, fine-tuned by Faishal-Anwar with a 32768 token context length. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for specialized applications, likely in the legal domain given its name, and offers efficient performance due to its optimized training process.
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Model Overview
This model, developed by Faishal-Anwar, is a fine-tuned version of the Qwen2.5-1.5B architecture. It leverages the unsloth/qwen2.5-1.5b-unsloth-bnb-4bit as its base and was trained using Unsloth and Huggingface's TRL library. This combination allowed for a significantly faster training process, reportedly 2x faster.
Key Characteristics
- Base Model: Qwen2.5-1.5B
- Parameter Count: 1.5 billion
- Context Length: 32768 tokens
- Training Optimization: Utilizes Unsloth for accelerated fine-tuning.
- License: Apache-2.0
Potential Use Cases
Given the model's name, "pgabl-legal-sft-faishal," it is likely specialized for tasks within the legal domain. Its efficient training and moderate parameter count make it suitable for applications requiring a balance of performance and resource efficiency, particularly where domain-specific knowledge is crucial.