pbeart/magictokens_finetune_merged
pbeart/magictokens_finetune_merged is a 1 billion parameter Llama-3.2-3B-Instruct-based model, developed by pbeart. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its Llama architecture and efficient fine-tuning process.
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Model Overview
pbeart/magictokens_finetune_merged is a 1 billion parameter language model, fine-tuned by pbeart. It is based on the unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit architecture, indicating its foundation in the Llama family of models. The fine-tuning process utilized Unsloth and Huggingface's TRL library, which significantly accelerated training by a factor of two.
Key Characteristics
- Architecture: Llama-3.2-3B-Instruct base model.
- Parameter Count: 1 billion parameters.
- Training Efficiency: Fine-tuned with Unsloth for 2x faster training.
- License: Released under the Apache-2.0 license.
Use Cases
This model is suitable for general instruction-following applications, benefiting from its Llama-based instruction-tuned foundation and efficient fine-tuning. Its smaller parameter count makes it a candidate for scenarios where computational resources are a consideration, while still providing capabilities derived from its Llama lineage.