excepto64/em-test
The excepto64/em-test is a 0.5 billion parameter instruction-tuned Qwen2.5 model developed by excepto64, fine-tuned using Unsloth and Huggingface's TRL library. This model is optimized for faster training, leveraging Unsloth's capabilities. It is suitable for applications requiring a compact yet capable language model with a 32768 token context length.
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Model Overview
The excepto64/em-test is a 0.5 billion parameter Qwen2.5-Instruct model, developed by excepto64. It was fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library, enabling significantly faster training times.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-0.5B-Instruct. - Training Efficiency: Leverages Unsloth for 2x faster training.
- Parameter Count: 0.5 billion parameters, making it a compact model.
- Context Length: Supports a context window of 32768 tokens.
Use Cases
This model is well-suited for applications where a smaller, efficiently trained instruction-following language model is beneficial. Its compact size and optimized training process make it a good candidate for resource-constrained environments or tasks requiring quick iteration and deployment.