ermiaazarkhalili/VibeThinker-3B-SFT-Fable5-Glint
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 26, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The ermiaazarkhalili/VibeThinker-3B-SFT-Fable5-Glint is a 3.1 billion parameter Qwen2-based causal language model with a 32768 token context length. Developed by ermiaazarkhalili, this model was fine-tuned from WeiboAI/VibeThinker-3B using Unsloth and Huggingface's TRL library. Its primary differentiator is its efficient training process, achieving 2x faster fine-tuning, making it suitable for applications requiring a compact yet capable language model.
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ermiaazarkhalili/VibeThinker-3B-SFT-Fable5-Glint Overview
This model, developed by ermiaazarkhalili, is a 3.1 billion parameter language model based on the Qwen2 architecture. It was fine-tuned from the WeiboAI/VibeThinker-3B model, leveraging the Unsloth library and Huggingface's TRL for an optimized training process.
Key Capabilities
- Efficient Training: Achieved 2x faster fine-tuning compared to standard methods, thanks to the integration of Unsloth and Huggingface's TRL library.
- Compact Size: With 3.1 billion parameters, it offers a balance between performance and computational efficiency.
- Extended Context Window: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
- Apache-2.0 License: Provides flexibility for both commercial and non-commercial use.
Good For
- Applications requiring a capable language model with a smaller footprint.
- Scenarios where efficient fine-tuning and deployment are critical.
- Tasks benefiting from a model with a substantial context window for detailed understanding.