amityco/matching-1.0-4b-sft
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Apr 9, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The amityco/matching-1.0-4b-sft is a 4 billion parameter Qwen3-based causal language model developed by amityco, fine-tuned from unsloth/Qwen3-4B-Thinking-2507. This model was trained using Unsloth for accelerated performance, offering a 32768 token context length. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient training methodology.
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Overview
The amityco/matching-1.0-4b-sft is a 4 billion parameter language model developed by amityco. It is built upon the Qwen3 architecture and was fine-tuned from the unsloth/Qwen3-4B-Thinking-2507 model. A key characteristic of this model's development is its training process, which utilized Unsloth to achieve a 2x speedup in training time.
Key Capabilities
- Qwen3 Architecture: Leverages the robust capabilities of the Qwen3 model family.
- Efficient Training: Benefits from Unsloth's optimization for faster training, potentially leading to more agile development cycles.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Good For
- General Language Tasks: Suitable for a broad range of applications requiring natural language understanding and generation.
- Developers seeking Qwen3-based models: Offers a fine-tuned variant of Qwen3 with a focus on efficient training.
- Applications requiring a large context window: The 32768 token context length is beneficial for tasks involving extensive text analysis or long-form content generation.