kabilesh-c/daedalus-designer
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Apr 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
kabilesh-c/daedalus-designer is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by kabilesh-c. Finetuned using Unsloth and Huggingface's TRL library, this model was trained 2x faster than standard methods. It is designed for general instruction-following tasks, leveraging its efficient training for practical applications.
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Model Overview
kabilesh-c/daedalus-designer is a 1.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by kabilesh-c, this model was finetuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit.
Key Characteristics
- Efficient Training: This model was trained significantly faster, specifically 2x faster, by utilizing the Unsloth library in conjunction with Huggingface's TRL library. This efficiency in training can lead to more accessible and quicker iteration cycles for developers.
- Parameter Count: With 1.5 billion parameters, it offers a balance between performance and computational resource requirements, making it suitable for various applications where larger models might be too resource-intensive.
- Context Length: The model supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining coherence and understanding.
Good For
- Instruction Following: As an instruction-tuned model, it is well-suited for tasks requiring it to follow specific commands or prompts to generate desired outputs.
- Resource-Efficient Deployment: Its relatively smaller size and efficient training methodology make it a strong candidate for deployment in environments with limited computational resources or for applications requiring faster inference times.
- Rapid Prototyping: The faster training process facilitated by Unsloth suggests its utility for developers looking to quickly finetune and experiment with models for specific use cases.