sj0727kim/toolcalling-merged-demo
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Apr 2, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The sj0727kim/toolcalling-merged-demo is a 2 billion parameter Qwen3 model developed by sj0727kim, fine-tuned from unsloth/Qwen3-1.7B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture for efficient processing.
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Model Overview
This model, developed by sj0727kim, is a 2 billion parameter Qwen3-based language model. It was fine-tuned from the unsloth/Qwen3-1.7B-unsloth-bnb-4bit base model, indicating an optimization for efficient resource usage.
Key Characteristics
- Architecture: Based on the Qwen3 family of models.
- Parameter Count: Features 2 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Training was accelerated by 2x using Unsloth and Huggingface's TRL library, highlighting a focus on rapid development and iteration.
- Context Length: Supports a context length of 32768 tokens, suitable for handling moderately long inputs.
Potential Use Cases
- General Language Tasks: Suitable for a wide range of natural language processing applications.
- Resource-Efficient Deployment: Its optimized training and parameter count make it a candidate for scenarios where computational resources are a consideration.
- Further Fine-tuning: Can serve as a strong base for additional fine-tuning on specific downstream tasks due to its efficient training methodology.