egoigo/Qwen3.5-2B-Base-MED-ChatVector
VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 30, 2026Architecture:Transformer Featherless Exclusive Cold
egoigo/Qwen3.5-2B-Base-MED-ChatVector is a 2.3 billion parameter language model with a 32768 token context length. This model is based on the Qwen3.5 architecture, designed for general language understanding and generation tasks. Its base nature suggests it is suitable for further fine-tuning on specific downstream applications, particularly those requiring a balance of performance and computational efficiency.
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Model Overview
This model card describes egoigo/Qwen3.5-2B-Base-MED-ChatVector, a 2.3 billion parameter language model. It is built upon the Qwen3.5 architecture, offering a substantial context window of 32768 tokens. As a base model, it is intended as a foundational component for various natural language processing tasks.
Key Capabilities
- General Language Understanding: Capable of processing and interpreting diverse textual inputs.
- Text Generation: Can generate coherent and contextually relevant text.
- Large Context Window: Supports processing long sequences of text, up to 32768 tokens, which is beneficial for tasks requiring extensive context.
- Foundation Model: Designed to be a robust base for further fine-tuning and adaptation to specific applications.
Good For
- Research and Development: Ideal for researchers exploring new NLP techniques or fine-tuning strategies.
- Custom Application Development: Suitable for developers who need a powerful base model to adapt for specialized use cases, such as domain-specific chatbots, content generation, or advanced text analysis.
- Resource-Efficient Deployment: With 2.3 billion parameters, it offers a balance between performance and computational requirements, making it potentially suitable for scenarios where larger models are impractical.