ckaskr/qwen-3b-brain-v1
The ckaskr/qwen-3b-brain-v1 is a 3.1 billion parameter instruction-tuned causal language model based on the Qwen2.5 architecture. This model was fine-tuned and converted to GGUF format using Unsloth, enabling efficient deployment and faster training. It is designed for general language tasks, offering a balance of performance and resource efficiency.
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Model Overview
The ckaskr/qwen-3b-brain-v1 is a 3.1 billion parameter language model built upon the Qwen2.5 architecture. It has been specifically instruction-tuned and optimized for efficient deployment and faster training through the use of Unsloth for fine-tuning and GGUF conversion.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: 3.1 billion parameters, offering a compact yet capable model size.
- Context Length: Supports a context window of 32768 tokens.
- Optimization: Fine-tuned with Unsloth, which facilitates 2x faster training and efficient GGUF format conversion.
- Deployment: Includes an Ollama Modelfile for straightforward deployment, supporting both text-only and multimodal (via
llama-mtmd-cli) use cases.
Use Cases
This model is suitable for a variety of general language generation and understanding tasks where a balance between performance and computational resources is desired. Its GGUF format and Ollama support make it particularly convenient for local inference and integration into applications requiring efficient model loading.