dragonstorm123/qwen3.5-4b-sft
VISIONConcurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The dragonstorm123/qwen3.5-4b-sft is a 4.5 billion parameter Qwen3.5 model, fine-tuned by dragonstorm123. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x speed improvement during the fine-tuning process. It is designed for general language tasks, leveraging the Qwen3.5 architecture for efficient performance.
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Overview
The dragonstorm123/qwen3.5-4b-sft is a 4.5 billion parameter language model, fine-tuned by dragonstorm123. It is based on the Qwen3.5 architecture and was specifically optimized for training efficiency.
Key Characteristics
- Base Model: Fine-tuned from
Qwen/Qwen3.5-4B. - Training Efficiency: Achieved 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library.
- Parameters: Contains 4.5 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
- General Language Generation: Suitable for a wide range of text generation tasks due to its Qwen3.5 foundation.
- Efficient Deployment: Its optimized training process suggests potential for efficient fine-tuning on custom datasets for specific applications.
- Research and Development: Can serve as a base for further experimentation and fine-tuning, particularly for those interested in efficient training methodologies.