Svngoku/youtube-summarizer-qwen3-4b
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The Svngoku/youtube-summarizer-qwen3-4b is a 4 billion parameter Qwen3 instruction-tuned causal language model developed by Svngoku. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is specifically designed for summarizing YouTube content, leveraging its Qwen3 architecture and efficient fine-tuning process.
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Model Overview
The Svngoku/youtube-summarizer-qwen3-4b is a 4 billion parameter Qwen3 instruction-tuned model, developed by Svngoku. It has a context length of 32768 tokens. This model was fine-tuned from unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit using the Unsloth library and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for accelerated training, making it resource-efficient.
- Qwen3 Architecture: Built upon the Qwen3 foundation, providing robust language understanding capabilities.
- Instruction-tuned: Optimized to follow instructions effectively for specific tasks.
Good For
- YouTube Content Summarization: Its primary design and fine-tuning focus make it suitable for generating concise summaries of YouTube videos or transcripts.
- Applications requiring efficient Qwen3 models: Ideal for developers looking for a Qwen3-based model that has undergone optimized fine-tuning for specific use cases.