Terisara/PAD_student_teacher_m2
Terisara/PAD_student_teacher_m2 is a 3.1 billion parameter instruction-tuned causal language model, finetuned and converted to GGUF format using Unsloth. This model is optimized for efficient deployment and faster training, making it suitable for local inference applications. Its primary use case is general instruction following, leveraging its compact size and GGUF format for accessibility.
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Overview
Terisara/PAD_student_teacher_m2 is a 3.1 billion parameter instruction-tuned language model. It has been specifically finetuned and converted into the GGUF format, which is ideal for efficient local inference on various hardware.
Key Characteristics
- Efficient Training: The model was trained using Unsloth, which facilitated a 2x faster training process.
- GGUF Format: Provided in GGUF format, ensuring compatibility with tools like
llama-cppandollamafor easy deployment. - Instruction-Tuned: Designed to follow instructions effectively, making it versatile for various NLP tasks.
- Compact Size: With 3.1 billion parameters, it offers a balance between performance and resource efficiency.
Deployment and Usage
This model is particularly well-suited for local deployment. An Ollama Modelfile is included, simplifying the setup process for users. Example command-line usage is provided for both text-only LLMs and multimodal models, indicating its potential for integration into different applications.
Good For
- Local Inference: Excellent for running on consumer-grade hardware due to its GGUF format and optimized size.
- Instruction Following: Capable of handling a wide range of instruction-based prompts.
- Developers: Ideal for those looking for an easily deployable and efficient model for experimentation or integration into applications.