AbhiCommits/claimwise-qwen35-4b
VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 27, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The AbhiCommits/claimwise-qwen35-4b is a 4.5 billion parameter Qwen3.5 model, finetuned by AbhiCommits. This model was optimized for training speed using Unsloth and Huggingface's TRL library, making it 2x faster to fine-tune. It is designed for applications requiring efficient deployment of a Qwen3.5-based language model with a 32768 token context length.
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Model Overview
AbhiCommits/claimwise-qwen35-4b is a 4.5 billion parameter language model, finetuned by AbhiCommits. It is based on the Qwen3.5 architecture and utilizes a substantial context length of 32768 tokens, making it suitable for processing longer inputs and generating comprehensive outputs.
Key Characteristics
- Architecture: Finetuned from the
unsloth/Qwen3.5-4Bbase model. - Parameter Count: Features 4.5 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a generous context window of 32768 tokens, enabling it to handle extensive textual information.
- Training Efficiency: The model was finetuned with Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
Potential Use Cases
- Efficient Deployment: Ideal for developers seeking a Qwen3.5-based model that can be rapidly finetuned and deployed.
- Long Context Tasks: Well-suited for applications requiring the processing of lengthy documents, conversations, or code snippets due to its large context window.
- Research and Development: Provides a solid foundation for further experimentation and finetuning on specific downstream tasks, leveraging its optimized training methodology.