Exploring AI
LLM Model Summary
Lets have a quick look of LLM Feature of an Example LLM
https://huggingface.co/moonshotai/Kimi-K3
Understanding the Model Architecture & Specifications
| Feature / Parameter | Model Specification | Alternative Types / Variants | Concept, Deep Dive & Analogy |
|---|---|---|---|
| Architecture | Mixture-of-Experts (MoE) | Dense Models, Linear/Recurrent Attention (Mamba/RWKV), Hybrid MoE-SSM (Jamba) | Concept: Routes inputs to specialized sub-networks ("experts") instead of using the entire model. Analogy: A hospital where a triage nurse sends you to 16 specialized doctors rather than calling every doctor in the building. |
| Total Parameters | 2.8T | Medium (70B), Large (405B), Ultra-Large (1T+) | Concept: The total capacity and stored knowledge base across all sub-networks. Analogy: A university library holding 2.8 million total reference books. |
| Activated Parameters | 104B | Fully Dense (100% activation), Low-Sparsity MoE (e.g., Mixtral 8x7B) | Concept: The actual subset of parameters powered on and calculated for a single token. Analogy: Pulling only 104 books off the library shelves to answer a single research question. |
| Number of Layers | 93 | Shallow (24–32 layers), Deep (80–100+ layers) | Concept: The sequential processing steps that refine information from input to output. Analogy: A 93-stage assembly line for custom vehicles. |
| Number of Dense Layers | 1 | Fully Dense (all layers dense), Fully Sparse (0 dense layers) | Concept: Layers where 100% of parameters are active for every token. Analogy: The universal inspection bay where every car must stop before entering specialized routing. |
| Attention-Layer Composition | 69 KDA + 24 Gated MLA | Standard MHA Composition, Pure GQA Composition | Concept: Combines Kernelized Attention (fast context scanning) with Gated Latent Attention (memory compression). Analogy: Combining speed-reading techniques with a shorthand note-taking system. |
| Attention Hidden Dimension | 7168 | Small (2048), Standard (4096), Ultra-Wide (7168+) | Concept: The width/capacity of the vector representing concepts in attention layers. Analogy: The level of granular detail recorded on a single page of notes. |
| Number of Attention Heads | 96 | Small (16–32 heads), Large (64–96 heads) | Concept: Parallel streams looking for different contextual relationships simultaneously. Analogy: 96 separate inspectors examining a contract at the same time for different details. |
| Latent MoE Dimension | 3584 | Compressed (1024–2048), Standard (3000–4000) | Concept: The vector size fed into the expert router to determine routing decisions. Analogy: The summary sheet given to a dispatcher to route a service call. |
| MoE Hidden Dimension (per Expert) | 3072 | Large Expert (8192+), Granular Expert (2048–4096) | Concept: The internal processing capacity of each individual expert module. Analogy: The workstation size and toolkit available to each specialized doctor. |
| Number of Experts | 896 | Few Large Experts (8–16), Granular Experts (64–1000+) | Concept: The total pool of specialized expert sub-networks in MoE layers. Analogy: A labor pool of 896 unique tradespeople available for hire. |
| Selected Experts per Token | 16 | Single Expert (Top-1), Dual Expert (Top-2), High Granularity (Top-16) | Concept: How many experts are chosen to process each individual word/token. Analogy: Dispatching exactly 16 specific trade contractors to handle a home renovation project. |
| Number of Shared Experts | 2 | No Shared Experts, Multi-Shared Experts | Concept: Experts permanently active for every token to handle universal logic. Analogy: Two master supervisors who remain on-site continuously regardless of who is working. |
| Vocabulary Size | 160K | Small (32K), Medium (128K), Ultra-Large (256K+) | Concept: The lookup dictionary of unique characters, words, and code snippets. Analogy: Having a dictionary that recognizes entire complex technical jargon terms as single words. |
| Context Length | 1048576 (1M+) | Standard (8K–32K), Extended (128K), Ultra-Long (1M–2M+) | Concept: The active memory window the model can process in a single request. Analogy: A massive whiteboard spanning an entire room that holds several full books at once. |
| Attention Mechanism | KDA & Gated MLA | Standard MHA, Grouped-Query Attention (GQA), FlashAttention | Concept: Optimized mathematical algorithms to prevent VRAM running out during long contexts. Analogy: A smart filing system that compresses archived documents without losing key facts. |
| Activation Function | SiTU-GLU | ReLU, GELU, SwiGLU | Concept: The mathematical gate deciding neuron firing intensity. Analogy: A smart dimmer switch that fine-tunes electrical signal flow based on context. |
| Vision Encoder | MoonViT-V2 | CLIP (OpenAI), SigLIP, Native Multimodal Tokenizers | Concept: A vision transformer that turns visual pixels into vector concepts for the text model. Analogy: An expert court investigator who describes evidence photos to a judge. |
| Parameters of Vision Encoder | 401M | Compact (80M–100M), Standard (300M–400M), Large (1B+) | Concept: The parameter size dedicated solely to image processing. Analogy: The specialized training level of the court investigator. |
| Quantization | MXFP4 weights / MXFP8 activations (QAT) | FP16 (Uncompressed), INT4/INT8 Post-Training Quantization (PTQ) | Concept: Ultra-compressed numerical precision trained natively to save hardware memory. Analogy: Pre-fabricating modular building blocks from standard sizes so construction uses minimal waste. |
| Modality | Text, Image | Text-Only, Audio-Text, Any-to-Any Omnimodal | Concept: The input and output data formats the model natively understands. Analogy: A bilingual assistant fluent in both written text and visual diagrams. |