AFP7/Qwen-Indo-SFT
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
AFP7/Qwen-Indo-SFT is a 3.1 billion parameter Qwen2.5-based instruction-tuned causal language model developed by AFP7. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and efficient fine-tuning process.
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Model Overview
AFP7/Qwen-Indo-SFT is a 3.1 billion parameter instruction-tuned language model developed by AFP7. It is based on the Qwen2.5 architecture and was fine-tuned from the unsloth/Qwen2.5-3B-Instruct-bnb-4bit model. A key differentiator in its development is the use of Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Capabilities
- Instruction Following: Designed to respond to and follow instructions effectively, building upon its Qwen2.5 base.
- Efficient Training: Benefits from optimization techniques provided by Unsloth, leading to quicker fine-tuning cycles.
- Qwen2.5 Architecture: Inherits the robust capabilities and performance characteristics of the Qwen2.5 model family.
Good For
- General-purpose instruction-based tasks: Suitable for applications requiring a model to understand and execute various commands.
- Developers seeking efficient Qwen2.5 derivatives: Ideal for those looking for models fine-tuned with performance-enhancing tools like Unsloth.
- Experimentation with Qwen2.5 models: Provides a readily available, fine-tuned variant for testing and deployment.