Terisara/PAD_Student_and_teacher

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Mar 19, 2026Architecture:Transformer Warm

Terisara/PAD_Student_and_teacher is a 3.1 billion parameter language model, fine-tuned and converted to GGUF format. This model leverages the Qwen2.5-3B-Instruct architecture, optimized for efficient deployment and usage. It is specifically designed for instructional tasks, offering a balance of performance and resource efficiency. The model benefits from accelerated training via Unsloth, making it suitable for applications requiring quick iteration and deployment.

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Model Overview

Terisara/PAD_Student_and_teacher is a 3.1 billion parameter language model, based on the Qwen2.5-3B-Instruct architecture. It has been fine-tuned and converted into the GGUF format, making it highly compatible with various inference engines, including llama-cli and Ollama.

Key Characteristics

  • Architecture: Utilizes the Qwen2.5-3B-Instruct base model, known for its instructional capabilities.
  • Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational demands.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs.
  • GGUF Format: Provided in GGUF format (specifically qwen2.5-3b-instruct.Q5_K_M.gguf), ensuring broad compatibility and efficient local deployment.
  • Accelerated Training: The model was trained using Unsloth, which facilitated a 2x faster training process.

Deployment and Usage

  • Command-Line Interface: Easily usable with llama-cli for text-only applications or llama-mtmd-cli for multimodal models, supporting Jinja templating.
  • Ollama Integration: Includes an Ollama Modelfile for straightforward deployment within the Ollama ecosystem.

Good For

  • Instruction-Following Tasks: Optimized for scenarios requiring the model to follow specific instructions.
  • Resource-Constrained Environments: Its 3.1B parameter size and GGUF format make it suitable for deployment on consumer-grade hardware.
  • Rapid Prototyping: The efficient training and deployment options make it ideal for quick development cycles.

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