whcho33/my-brain-v1
whcho33/my-brain-v1 is a 3.1 billion parameter instruction-tuned Qwen2.5 causal language model developed by whcho33. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and performance, making it suitable for applications requiring a compact yet capable language model.
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Model Overview
whcho33/my-brain-v1 is an instruction-tuned language model based on the Qwen2.5 architecture, featuring 3.1 billion parameters. Developed by whcho33, this model was specifically finetuned using the Unsloth library in conjunction with Huggingface's TRL library. This training approach allowed for a significant acceleration in the finetuning process, achieving speeds up to 2x faster compared to conventional methods.
Key Characteristics
- Architecture: Qwen2.5-3B-Instruct base model.
- Parameter Count: 3.1 billion parameters.
- Training Efficiency: Utilizes Unsloth for accelerated finetuning, resulting in 2x faster training times.
- Context Length: Supports a context window of 32768 tokens.
- License: Distributed under the Apache-2.0 license.
Intended Use Cases
This model is well-suited for applications where a balance between performance and computational efficiency is crucial. Its optimized training process suggests it can be effectively deployed in scenarios requiring a capable instruction-following model without the overhead of larger models. Potential applications include:
- Instruction-based text generation.
- Chatbot development.
- Summarization and question-answering tasks.
- Edge device deployment or resource-constrained environments due to its compact size.