rach05/annual_report_ft
The rach05/annual_report_ft is a 4.5 billion parameter Qwen3.5 causal language model developed by rach05, fine-tuned from unsloth/Qwen3.5-4B. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. It is designed for text generation tasks, leveraging its Qwen3.5 architecture and efficient training methodology.
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Model Overview
The rach05/annual_report_ft is a 4.5 billion parameter language model, fine-tuned by rach05 from the unsloth/Qwen3.5-4B base model. This model leverages the Qwen3.5 architecture and was specifically trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster fine-tuning process.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen3.5-4B. - Training Efficiency: Utilizes Unsloth for accelerated fine-tuning, resulting in 2x faster training times.
- Architecture: Based on the Qwen3.5 model family.
- License: Distributed under the Apache-2.0 license.
Use Cases
This model is primarily intended for general text generation tasks, benefiting from its efficient fine-tuning process and the capabilities inherited from the Qwen3.5 base model. Its efficient training makes it a suitable candidate for applications requiring a Qwen3.5-based model with optimized development cycles.