JAM_Intel_1b: An Efficiently Fine-Tuned Qwen2.5 Model

JAM_Intel_1b is a 1.5 billion parameter instruction-tuned language model, developed by jramsartificialmodel. It is based on the Qwen2.5 architecture and has been fine-tuned from unsloth/qwen2.5-1.5b-instruct-bnb-4bit.

Key Characteristics

• Architecture: Qwen2.5-based causal language model.
• Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
• Context Length: Supports a context window of 32768 tokens.
• Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
• License: Released under the Apache-2.0 license, allowing for broad usage and distribution.

Use Cases

This model is suitable for a variety of general instruction-following tasks where a compact yet capable language model is required. Its efficient training process suggests it could be a good candidate for applications needing rapid iteration or deployment on resource-constrained environments.