Table of Contents

⚖️ What are Weights in Large Language Models?


📄 TL;DR

Weights are the numerical parameters stored within a neural network that encode everything the model has learned during training. In Large Language Models (LLMs), weights determine how input text is transformed into meaningful predictions. The size of a model file is primarily determined by its weights, and techniques like quantization reduce precision to shrink file sizes and accelerate inference. LM-Kit provides comprehensive weight management through support for multiple quantization formats and LoRA adapter integration to adapt models to specific use cases.


🧠 What Are Weights?

When an LLM processes text, it passes data through billions of mathematical operations. Each operation involves multiplying inputs by learned parameters called weights. These weights were adjusted during training to minimize prediction errors, and they collectively store the model's "knowledge."

Think of weights as the model's memory:

  • Learned patterns: Weights encode grammar, facts, reasoning patterns, and stylistic tendencies.
  • Layer-by-layer transformation: Each layer of the model uses its own set of weights to progressively refine the representation of input text.
  • Deterministic behavior: Given the same input and weights, the model produces the same output (assuming deterministic sampling).

⚙️ How Are Weights Structured?

Weights are organized into tensors (multi-dimensional arrays) distributed across the model's architecture:

  1. Embedding Weights: Convert input tokens into dense vector representations that the model can process.

  2. Attention Weights: Control how the model relates different parts of the input sequence to each other (query, key, and value projections).

  3. Feed-Forward Weights: Transform representations between attention layers, enabling non-linear reasoning.

  4. Output Weights: Project the final hidden state back to vocabulary space to predict the next token.


📊 Weight Precision and Data Types

Weights are stored using various numerical formats, each with different precision levels:

Format Bits per Weight Description
FP32 32 bits Full precision (training default)
FP16 16 bits Half precision (common for inference)
BF16 16 bits Brain float (better range than FP16)
INT8 8 bits 8-bit integer (quantized)
INT4 4 bits 4-bit integer (heavily quantized)

The lower the precision, the smaller the model file and faster the inference, but with potential accuracy trade-offs.


💡 Why Do Weights Matter?

Understanding weights is essential for:

  • Memory Planning: A 7B parameter model in FP16 requires ~14 GB of RAM; in 4-bit quantization, only ~4 GB.
  • Performance Tuning: Choosing the right quantization level balances quality and speed.
  • Model Selection: Matching model size to your hardware capabilities ensures smooth inference.
  • LoRA Adapters: Adding small trainable weight matrices enables efficient customization.

Memory Requirements by Precision:

Model Size FP16 8-bit (Q8) 4-bit (Q4)
3B params ~6 GB ~3 GB ~1.5 GB
7B params ~14 GB ~7 GB ~4 GB
13B params ~26 GB ~13 GB ~7 GB
70B params ~140 GB ~70 GB ~35 GB

🔧 Weight Management in LM-Kit

LM-Kit.NET provides robust tools for working with model weights efficiently:

📦 GGUF Format Support

LM-Kit.NET uses the GGUF format, which stores weights alongside model metadata in a single, portable file. GGUF supports both quantized and non-quantized weights:

// Load a quantized model by catalog ID (auto-downloads if needed)
using LM model = LM.LoadFromModelID("gemma3:4b");

The model file contains:

  • All weight tensors in the specified precision
  • Vocabulary and tokenizer data
  • Architecture metadata and configuration

See the LM class documentation for all available loading options.


⚡ Quantization Tools

LM-Kit.NET includes a built-in quantization engine to convert model weights to lower precision:

// Quantize a model to 4-bit precision
var quantizer = new LMKit.Quantization.ModelQuantizer();
quantizer.Quantize("model-fp16.gguf", "model-Q4_K_M.gguf", QuantizationType.MOSTLY_Q4_K_M);

Supported Quantization Formats:

Format Quality Size Recommended For
Q2_K Low Smallest Extreme memory constraints
Q3_K_M Medium-Low Very small Mobile/IoT devices
Q4_K_M Good Small General use ✅
Q5_K_M Very Good Medium Quality-focused applications ✅
Q6_K Excellent Large Near-lossless inference
Q8_0 Near-Original Very large Maximum quality

🔄 LoRA Adapter Integration

Instead of modifying base model weights directly, LoRA (Low-Rank Adaptation) adds small trainable weight matrices that can be dynamically applied:

// Load base model
var model = new LMKit.Model.LM("base-model.gguf");

// Apply a LoRA adapter
model.ApplyLoraAdapter("custom-adapter.gguf", scale: 1.0f);

// Adapters can be removed or swapped at runtime
model.RemoveLoraAdapter(adapter);

Benefits of LoRA:

  • ✅ Keep base weights frozen
  • ✅ Small adapter files (typically < 100 MB)
  • ✅ Stack multiple adapters
  • ✅ Adjust influence with scaling factor

You can also merge LoRA adapters permanently into base models using the LoraMerger class.


🧩 Weights in the Model Loading Pipeline

When LM-Kit.NET loads a model, here is what happens with weights:

  1. File Parsing: GGUF metadata is read to determine architecture and weight layout.

  2. Memory Allocation: RAM/VRAM is allocated based on weight tensor sizes and precision.

  3. Weight Loading: Tensors are streamed into memory, optionally distributed across multiple GPUs.

  4. Dequantization (if needed): Quantized weights may be partially dequantized during inference for computation.

// Control GPU layer distribution
var config = new LM.DeviceConfiguration 
{
    GpuLayerCount = 32  // Number of layers to offload to GPU
};

var model = new LMKit.Model.LM("model.gguf", config);

// Access model properties
Console.WriteLine($"Parameters: {model.ParameterCount:N0}");
Console.WriteLine($"Size: {model.Size / 1_000_000_000.0:F2} GB");
Console.WriteLine($"Layers: {model.LayerCount}");

For guidance on selecting the right model for your hardware, see Choosing the Right Language Model.


📝 Summary

Weights are the learned numerical parameters that form the core of any neural network. In LLMs, weights encode language understanding, reasoning capabilities, and generation quality.

  • ⚖️ Weights determine the model's knowledge and behavior.
  • 📉 Quantization reduces weight precision for smaller files and faster inference.
  • 🔧 LM-Kit.NET supports weights in GGUF format with multiple quantization levels.

In LM-Kit:

  • Models can be loaded with various quantization precisions (Q4, Q5, Q8, etc.) from the model catalog.
  • LoRA adapters enable efficient weight customization without modifying base weights.
  • Built-in quantization tools convert models between precision formats.

Understanding weights helps you make informed decisions about model selection, memory requirements, and optimization strategies, enabling you to deploy AI models efficiently on any hardware, from edge devices to multi-GPU servers.