StarsMakeGalaxy/ragbench-expertqa-qwen3.5-4b
VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The StarsMakeGalaxy/ragbench-expertqa-qwen3.5-4b is a 4.5 billion parameter language model, finetuned from Qwen/Qwen3.5-4B by StarsMakeGalaxy. It was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. This model is designed for applications requiring efficient and optimized language processing, leveraging its Qwen 3.5 base and accelerated training methodology.
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Overview
StarsMakeGalaxy/ragbench-expertqa-qwen3.5-4b is a 4.5 billion parameter language model developed by StarsMakeGalaxy, finetuned from the Qwen/Qwen3.5-4B base model. It utilizes a 32768 token context length, making it suitable for tasks requiring substantial input understanding.
Key Capabilities
- Efficient Training: This model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library, demonstrating optimized resource utilization during fine-tuning.
- Qwen 3.5 Base: Built upon the robust Qwen 3.5 architecture, it inherits strong foundational language understanding and generation capabilities.
Good For
- Applications requiring efficient fine-tuning: Its accelerated training process makes it a good candidate for projects where rapid iteration and deployment of fine-tuned models are crucial.
- Tasks benefiting from a Qwen 3.5 foundation: Suitable for various natural language processing tasks that leverage the strengths of the Qwen 3.5 model family.