TUpreneur/basic-brain-SFT
The TUpreneur/basic-brain-SFT model is a 7.6 billion parameter instruction-tuned language model, based on the Qwen2.5-7B-Instruct architecture. It was fine-tuned and converted to GGUF format using Unsloth, which facilitated faster training. This model is designed for general text-based applications and can be easily deployed with tools like llama-cli or Ollama.
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TUpreneur/basic-brain-SFT Overview
This model, basic-brain-SFT, is an instruction-tuned language model built upon the Qwen2.5-7B-Instruct architecture. It features 7.6 billion parameters and supports a context length of 32768 tokens. A key aspect of its development is the use of Unsloth for fine-tuning and GGUF conversion, which significantly accelerated its training process.
Key Characteristics
- Architecture: Based on the robust Qwen2.5-7B-Instruct model.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and resource efficiency.
- Context Length: Supports a substantial 32768 tokens, enabling processing of longer inputs.
- Optimized Training: Fine-tuned with Unsloth, resulting in a 2x faster training time.
- GGUF Format: Provided in GGUF format, making it compatible with various inference engines and tools.
Deployment and Usage
- Easy Deployment: Includes an Ollama Modelfile for straightforward local deployment.
- CLI Compatibility: Can be used with
llama-clifor text-only applications orllama-mtmd-clifor multimodal models (if applicable to the base architecture).
This model is suitable for developers looking for an efficiently trained, instruction-following language model that is easy to integrate into their workflows.