grimculver/dicoding-genai
The grimculver/dicoding-genai is a 3.2 billion parameter Llama-based instruction-tuned causal language model developed by grimculver. Finetuned using Unsloth and Huggingface's TRL library, this model benefits from accelerated training. It is designed for general instruction-following tasks, leveraging its Llama architecture for versatile applications.
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grimculver/dicoding-genai: A Fast-Trained Llama Model
The grimculver/dicoding-genai is a 3.2 billion parameter instruction-tuned language model, developed by grimculver. It is based on the Llama architecture, specifically finetuned from unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Llama-based causal language model.
- Parameter Count: 3.2 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: This model was trained significantly faster (2x) by utilizing Unsloth and Huggingface's TRL library, indicating an optimized training process.
- Context Length: Supports a context window of 32768 tokens, allowing for processing longer inputs and generating more coherent responses.
Use Cases
This model is suitable for a variety of instruction-following tasks, benefiting from its Llama foundation and instruction-tuned nature. Its efficient training methodology suggests it could be a good candidate for applications where rapid iteration or deployment of finetuned models is crucial.