jramsartificialmodel/JAM_Intel_1b
JAM_Intel_1b is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by jramsartificialmodel. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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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.