Title: Quiz 2 — Open Models, Licenses, and the Fine-Tuning Toolchain Setup
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 02 — Open Models, Licenses, and the Fine-Tuning Toolchain Setup

Q: A model with publicly downloadable weights is automatically "open source" in the OSI sense.
- True
- False
A: False

Q: Which of the following best describes Meta's Llama Community License?
- An OSI-approved open source license with no restrictions
- A custom license that is not OSI-approved and imposes a usage cap (700M MAU) along with other restrictions
- Identical in terms to the Apache 2.0 license
- A license that only applies to non-commercial research use
A: A custom license that is not OSI-approved and imposes a usage cap (700M MAU) along with other restrictions

Q: Which license type is used by models like Qwen 3, DeepSeek, and Mistral, allowing commercial use with no royalties or usage caps?
- Llama Community License
- Apache 2.0 / MIT
- Creative Commons NonCommercial
- GPL v3
A: Apache 2.0 / MIT

Q: Because two open-weight models perform similarly on benchmarks, they are equally safe to use in any commercial deployment.
- True
- False
A: False

Q: Before fine-tuning or commercially deploying any open model, where should you verify the exact license terms and usage restrictions?
Feedback: Sample/expected answer: Directly on the model's page/model card on Hugging Face (license terms can change between versions, so always check the current version)

Q: Which library is the unified post-training hub supporting SFT, reward modeling, DPO, and GRPO, and serves as the integration point for PEFT adapters and Transformers models?
- bitsandbytes
- Accelerate
- TRL
- datasets
A: TRL

Q: Which tool in the fine-tuning toolchain is specifically known for speed and memory efficiency on consumer-grade GPUs?
- Axolotl
- Unsloth
- Accelerate
- datasets
A: Unsloth

Q: Name one library in the 2026 fine-tuning toolchain used specifically for parameter-efficient fine-tuning methods like LoRA and QLoRA.
Feedback: Sample/expected answer: PEFT