MooJae/toolcalling-merged-demo
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Apr 9, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
MooJae/toolcalling-merged-demo is a 2 billion parameter Qwen3-based causal language model, fine-tuned by MooJae. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It features a 32768 token context length, making it suitable for tasks requiring extensive contextual understanding.
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Model Overview
MooJae/toolcalling-merged-demo is a 2 billion parameter language model, fine-tuned by MooJae. It is based on the Qwen3 architecture and was specifically trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster fine-tuning process. This model inherits the robust capabilities of the Qwen3 base model, enhanced by specialized training.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for significantly faster training, making it efficient for custom applications.
- Qwen3 Architecture: Built upon the Qwen3 model family, known for strong language understanding and generation.
- Extended Context Length: Supports a substantial context window of 32768 tokens, allowing it to process and generate longer, more coherent texts.
Good For
- Rapid Prototyping: Ideal for developers looking to quickly fine-tune and deploy Qwen3-based models.
- Applications Requiring Large Context: Suitable for tasks that benefit from processing extensive input, such as summarization of long documents or complex conversational agents.
- Research and Development: Provides a foundation for further experimentation with efficient fine-tuning techniques on Qwen3 models.