kumapo/qwen3-0.6b-sft-lora-rank2048-2phase
The kumapo/qwen3-0.6b-sft-lora-rank2048-2phase is a 0.8 billion parameter Qwen3 model developed by kumapo, fine-tuned using Unsloth and Huggingface's TRL library. This model is optimized for efficient training, achieving 2x faster finetuning. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable base for various applications.
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Model Overview
The kumapo/qwen3-0.6b-sft-lora-rank2048-2phase is a 0.8 billion parameter Qwen3 model developed by kumapo. It has been fine-tuned using a two-phase process with LoRA (rank 2048) and leverages the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Efficient Training: This model was trained significantly faster, achieving a 2x speedup, by utilizing Unsloth's optimization techniques.
- Base Model: It is fine-tuned from
unsloth/qwen3-0.6b-unsloth-bnb-4bit, indicating a foundation in the Qwen3 architecture. - Parameter Count: With 0.8 billion parameters, it offers a balance between performance and computational efficiency.
Use Cases
This model is suitable for applications where a compact yet capable language model is required, especially when efficient fine-tuning is a priority. Its optimized training process makes it a good candidate for rapid experimentation and deployment in resource-constrained environments or for tasks that benefit from quick iteration cycles.