Realline/toolcalling-merged-demo
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Mar 26, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
Realline/toolcalling-merged-demo is a 2 billion parameter Qwen3-based causal language model developed by Realline, fine-tuned using Unsloth and Huggingface's TRL library. This model was trained for enhanced efficiency, achieving 2x faster training speeds. It is designed for general language tasks, leveraging its Qwen3 architecture and optimized training process.
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Model Overview
Realline/toolcalling-merged-demo is a 2 billion parameter language model based on the Qwen3 architecture, developed by Realline. It was fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library, which enabled a significant acceleration in its training process, reportedly achieving 2x faster training speeds.
Key Characteristics
- Architecture: Qwen3-based, providing a robust foundation for various language understanding and generation tasks.
- Parameter Count: 2 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Utilizes Unsloth for optimized training, resulting in faster iteration cycles and potentially more accessible fine-tuning.
- Context Length: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Potential Use Cases
- General Language Generation: Suitable for a wide range of text generation tasks due to its Qwen3 base.
- Efficient Fine-tuning: The model's optimized training process suggests it could be a good candidate for further fine-tuning on specific downstream tasks where rapid experimentation is desired.
- Research and Development: Its foundation and training methodology make it relevant for researchers exploring efficient LLM development.