dcraver2005/qwen_sft_16bit
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:May 14, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The dcraver2005/qwen_sft_16bit is a 4 billion parameter Qwen3-based causal language model, finetuned by dcraver2005. This model was specifically optimized for faster training using Unsloth and Huggingface's TRL library, making it efficient for further fine-tuning or deployment in scenarios where training speed is critical. It is designed for general language generation tasks, leveraging the Qwen3 architecture.
Loading preview...
Model Overview
The dcraver2005/qwen_sft_16bit is a 4 billion parameter Qwen3-based language model, developed by dcraver2005. It was finetuned from unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit with a focus on training efficiency.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational requirements.
- Training Efficiency: This model was trained significantly faster (2x) using the Unsloth library in conjunction with Huggingface's TRL library. This indicates an optimization for rapid iteration and deployment.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating coherent, extended outputs.
Good For
- Rapid Prototyping: Its optimized training process makes it suitable for developers looking to quickly fine-tune and experiment with Qwen3-based models.
- General Language Tasks: Capable of various natural language generation and understanding tasks, benefiting from the robust Qwen3 architecture.
- Resource-Efficient Deployment: The 4B parameter size makes it a viable option for applications where computational resources are a consideration, while still offering strong performance.