amityco/tau-max-ds-sft
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Mar 17, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The amityco/tau-max-ds-sft is a 4 billion parameter Qwen3-based causal language model developed by amityco, fine-tuned for general language tasks. This model was efficiently trained using Unsloth and Huggingface's TRL library, offering a 32768 token context length. It is designed for applications requiring a compact yet capable language model with optimized training efficiency.
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Model Overview
The amityco/tau-max-ds-sft is a 4 billion parameter language model, fine-tuned from the unsloth/Qwen3-4B-Thinking-2507 base model. Developed by amityco, this model leverages the Qwen3 architecture and features a substantial 32768 token context length, making it suitable for processing longer sequences of text.
Key Capabilities
- Efficient Training: This model was fine-tuned with significant efficiency gains, achieving 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library. This indicates an optimized training process, potentially leading to faster iteration and deployment.
- Qwen3 Architecture: Built upon the Qwen3 family, it inherits the robust capabilities of this architecture, generally known for strong performance across various language understanding and generation tasks.
- General Purpose: As a fine-tuned model, it is prepared for a broad range of downstream applications requiring a capable language model.
Good For
- Resource-Efficient Deployment: Its 4 billion parameter size makes it a good candidate for applications where computational resources are a consideration, balancing performance with efficiency.
- Rapid Prototyping: The optimized training methodology suggests it could be beneficial for developers looking for models that are quick to fine-tune or adapt for specific use cases.
- General Language Tasks: Suitable for common NLP tasks such as text generation, summarization, question answering, and more, given its Qwen3 foundation and fine-tuned nature.