Llama LLM MCQ Questions and Answers

Q1. Who developed the Llama series of large language models?

A. OpenAI
B. Meta (Facebook)
C. Google
D. Anthropic
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Q2. What licensing approach did Meta take with Llama 2?

A. Fully proprietary with no public access
B. Open source under GPL license
C. Available for research only
D. Freely available for commercial use with certain restrictions
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Q3. What technique was used to make Llama 2-Chat versions safer for deployment?

A. Supervised Fine-Tuning (SFT) only
B. Reinforcement Learning from Human Feedback (RLHF)
C. Constitutional AI
D. Retrieval-Augmented Generation
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Q4. What was a key architectural advancement in Llama 2 compared to the original Llama?

A. Significantly increased training data volume
B. Replacement of transformer architecture with a new design
C. Integration of multimodal capabilities
D. Doubled context length
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Q5. Which size variants were released for Llama 2?

A. 7B, 13B, 34B, and 70B parameters
B. 7B, 13B, and 70B parameters
C. 7B, 13B, 33B, and 65B parameters
D. 3B, 7B, 13B, and 70B parameters
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Q6. What is "Llama-in-the-middle" evaluation?

A. Running Llama on edge devices
B. Testing Llama's resistance to prompt injection attacks
C. Using Llama to evaluate the outputs of other AI models
D. Training Llama on data extracted from the middle of documents
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Q7. What tokenizer does Llama use?

A. BPE (Byte Pair Encoding)
B. WordPiece
C. Unigram
D. SentencePiece
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Q8. What is a key advantage of Llama models compared to some other similar-sized LLMs?

A. They can run efficiently on consumer hardware
B. They require no fine-tuning for most tasks
C. They have built-in image understanding capabilities
D. They can generate code in all programming languages
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Q9. What notable improvement did Llama 2 make in terms of safety compared to Llama 1?

A. Addition of content filtering API
B. Pre-filtering of the training data
C. Enhanced safety specific RLHF training
D. Implementation of an external moderator model
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Q10. Which of these is NOT a version of the Llama model family?

A. Llama 2-Chat
B. Llama Code
C. Llama Vision
D. Llama 3
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Q11. What framework is commonly used to quantize Llama models for more efficient deployment?

A. TensorFlow Lite
B. ONNX Runtime
C. NVIDIA TensorRT
D. GGML/GGUF
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Q12. What is a key capability of Llama 3 compared to Llama 2?

A. Lower latency
B. Better performance on factual knowledge and reasoning tasks
C. Support for 200+ languages
D. Built-in image generation capabilities
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Q13. What is the context window length of Llama 3?

A. 4K tokens
B. 8K tokens
C. 16K tokens
D. 128K tokens
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Q14. Which popular application is built on top of Llama models?

A. ChatGPT
B. Claude
C. Meta AI
D. Bard
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Q15. What technique is used in Llama models to reduce training compute requirements?

A. Grouped-query attention
B. Mixture of Experts
C. Knowledge distillation
D. Sparse transformers
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Q16. What is the "Llama ecosystem"?

A. A hardware platform optimized for running Llama models
B. The community of developers and projects built around Llama models
C. Meta's subscription service for accessing Llama APIs
D. A specialized data center for training Llama models
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Q17. What is "Llama Factory"?

A. Meta's research lab where Llama models are developed
B. An open-source library for fine-tuning Llama models
C. A cloud service for deploying Llama at scale
D. A hardware specification for Llama-optimized servers
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Q18. What innovation did Meta introduce with Llama 3 for structured outputs?

A. JSON mode
B. XML generator
C. SQL compiler
D. Structured markup language
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Q19. What was the training approach used for Llama 2 that helped it balance helpfulness and safety?

A. Constitutional AI
B. Helpful and Harmless Alignment
C. Safety-focused fine-tuning only
D. Rule-based safety filters
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Q20. What is "TinyLlama"?

A. An official 1B parameter version of Llama
B. A community project to create a small, efficient Llama model
C. Meta's mobile-optimized version of Llama
D. A hardware accelerator for Llama models
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Q21. What technique is used to adapt Llama models for instruction following?

A. Hyperparameter optimization
B. Architecture modification
C. Instruction-tuning with supervised and RLHF training
D. Transfer learning from GPT models
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Q22. Which organization developed and maintains "llama.cpp"?

A. Meta
B. Hugging Face
C. Open source community (not affiliated with Meta)
D. Microsoft
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Q23. What is "Alpaca," in relation to Llama models?

A. A hardware acceleration card for Llama
B. A fine-tuned version of Llama focused on instruction-following
C. Meta's internal codename for Llama 3
D. A benchmark for testing Llama performance
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Q24. What distinguishes "Llama 2-Chat" from the base Llama 2 model?

A. It has more parameters
B. It's optimized for conversation through fine-tuning and alignment
C. It has a larger context window
D. It uses a different architecture
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Q25. What is "CodeLlama"?

A. A programming language designed for Llama models
B. A community-developed code generator based on Llama
C. Meta's code-specialized version of Llama
D. A code completion tool using API calls to Llama
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Q26. What is a limitation of the early Llama models compared to some competitors?

A. Lacking multimodal capabilities
B. Limited to English language only
C. Unable to follow basic instructions
D. Completely closed-source
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Q27. What is the recommended system prompt format for Llama 2-Chat models?

A. <system>instruction</system><user>message</user><assistant>response</assistant>
B. [INST] <<SYS>> instruction <</SYS>> message [/INST]
C. {system: "instruction", user: "message", assistant: "response"}
D. System: instruction\nUser: message\nAssistant: response
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Q28. What is "MMS-LLaMA" in relation to Llama models?

A. A mobile messaging service powered by Llama
B. A multilingual variant of Llama supporting 100+ languages
C. A Llama model with multimedia support
D. A model management system for Llama deployments
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