A model compression technique that reduces weight precision (e.g., from 16-bit to 4-bit) to decrease model size and inference cost while preserving most quality. Three dominant formats in 2024-2025: GGUF (flexible CPU/GPU format for llama.cpp), GPTQ (GPU-optimized post-training quantization), and AWQ (activation-aware weight quantization). All keep quality within ~6% of full-precision at 4-bit.
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A model compression technique that reduces weight precision (e.g., from 16-bit to 4-bit) to decrease model size and inference cost while preserving most quality. Three dominant formats in 2024-2025: GGUF (flexible CPU/GPU format for llama.cpp), GPTQ (GPU-optimized post-training quantization), and AWQ (activation-aware weight quantization). All keep quality within ~6% of full-precision at 4-bit.
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Quantization (AI) (quantization) Category: AI / ML Definition: A model compression technique that reduces weight precision (e.g., from 16-bit to 4-bit) to decrease model size and inference cost while preserving most quality. Three dominant formats in 2024-2025: GGUF (flexible CPU/GPU format for llama.cpp), GPTQ (GPU-optimized post-training quantization), and AWQ (activation-aware weight quantization). All keep quality within ~6% of full-precision at 4-bit. Aliases: GGUF, GPTQ, AWQ Related: Inference, Open-Source AI Models, Knowledge Distillation
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The process of running a trained model on new inputs to generate predictions or outputs. Inference is the 'using' phase (vs. training). Inference cost depends on model size, input/output token count, and hardware (GPUs/TPUs). API providers (Anthropic, OpenAI) charge per token for inference. On-device inference (llama.cpp, GGUF) runs locally without API calls.
AI models with publicly released weights that can be downloaded, modified, and self-hosted. Notable open models: Llama 3 (Meta), Mistral, Falcon, Gemma (Google), Phi (Microsoft). Open models enable privacy (data stays local), customization (fine-tuning), and cost control. Trade-off: generally less capable than frontier proprietary models but rapidly improving.
A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
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The process of running a trained model on new inputs to generate predictions or outputs. Inference is the 'using' phase (vs. training). Inference cost depends on model size, input/output token count, and hardware (GPUs/TPUs). API providers (Anthropic, OpenAI) charge per token for inference. On-device inference (llama.cpp, GGUF) runs locally without API calls.
AI models with publicly released weights that can be downloaded, modified, and self-hosted. Notable open models: Llama 3 (Meta), Mistral, Falcon, Gemma (Google), Phi (Microsoft). Open models enable privacy (data stays local), customization (fine-tuning), and cost control. Trade-off: generally less capable than frontier proprietary models but rapidly improving.
A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
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A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.