StarsMakeGalaxy/Qwen3.5-9B-RAG
VISIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 3, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
StarsMakeGalaxy/Qwen3.5-9B-RAG is a 9 billion parameter Qwen3.5-based language model developed by StarsMakeGalaxy, fine-tuned for RAG applications. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. With a 32768 token context length, it is optimized for efficient retrieval-augmented generation tasks.
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Model Overview
StarsMakeGalaxy/Qwen3.5-9B-RAG is a 9 billion parameter language model, fine-tuned from the unsloth/Qwen3.5-9B base model. Developed by StarsMakeGalaxy, this model leverages the Qwen3.5 architecture and is specifically optimized for Retrieval-Augmented Generation (RAG) workflows.
Key Capabilities
- Efficient Fine-tuning: This model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x speed improvement during the training process.
- RAG Optimization: Designed with RAG applications in mind, it is suitable for tasks requiring information retrieval and generation based on external knowledge sources.
- Context Length: Features a substantial context window of 32768 tokens, allowing for processing and understanding longer inputs and retrieved documents.
Good For
- Developers seeking a Qwen3.5-based model specifically fine-tuned for RAG tasks.
- Applications where efficient fine-tuning and a large context window are beneficial.
- Use cases requiring the integration of external data for more accurate and informed responses.