What LLM Contains

Downloading a model from Ollama or similar platforms (like Hugging Face, TensorFlow Hub, etc.) gives you access to more than just a file labeled “model.” These packages contain several components that allow the model to be executed, fine-tuned, or embedded in applications.

Let’s break it down into a clear structure, so you can understand, dissect, and even modify the model effectively.

🔹 1) What You Get When You Download a Model

A typical downloaded model (especially from Ollama, Hugging Face, etc.) consists of the following main components:

✅ A. Model Weights (Parameters)

Usually large binary files (e.g., .bin, .pt, .safetensors, .ckpt).

These contain the actual learned values (neurons, weights, biases) from training.

Cannot run the model without this.

Example:

pytorch_model.bin – For PyTorch
ggml-model-q4_0.bin – Quantized weights used in Ollama and llama.cpp

✅ B. Model Architecture / Config File

Defines the structure of the model (number of layers, hidden units, attention heads, etc.).

Often in a .json or .yaml file.

Example:

config.json – Specifies transformer type, vocabulary size, hidden dimensions, etc.

✅ C. Tokenizer Files

Preprocessing logic that turns text into tokens and vice versa.

Includes vocabulary (vocab.json), merges (merges.txt), or tokenizer config.

Example:

tokenizer.json
vocab.txt
merges.txt
tokenizer_config.json

✅ D. Generation Scripts or Runners

Python or binary files that allow you to run the model (with inference loops, prompts, sampling settings, etc.).

Ollama wraps this in a unified runtime using Modelfile and its CLI.

In frameworks like Hugging Face:

run_generation.py
model.py

✅ E. Quantization Information (Optional)

If you're using a quantized model (like from llama.cpp or ggml), there may be metadata about how the weights are compressed.

Affects performance and memory usage.

✅ F. Prompt Templates / System Instructions (Optional in LLMs)

Ollama models often include template prompts (like system, user, assistant roles).

These guide how prompts are injected before inference.

🔹 2) How You Can Divide and Understand It

Component	Purpose	Editable?	Tool Used
Model Weights	Learned knowledge of the model	❌ Not easily editable	Python, `llama.cpp`, Ollama
Config/Architecture	Structure of the neural network	✅ Yes	Text editor
Tokenizer	Converts words into model-readable format	✅ Yes	Hugging Face, `tiktoken`
Prompt Templates	Controls the input prompt formatting	✅ Yes	`Modelfile` in Ollama
Quantization Info	Enables smaller model sizes and faster runs	✅ Yes (with tools)	`llama.cpp`, `ggml`

🔹 3) If You're Using Ollama

When you ollama pull llama3, for example, behind the scenes it downloads a model package that contains:

✅ A quantized binary model file (.bin)

✅ A Modelfile (like a Dockerfile for models)

✅ Prompt format templates (like system, user, etc.)

You can run

ollama show llama3

To inspect the components and configuration.

🔹 4) How to Empower Yourself

Here’s how you can gain deeper control:

Goal	What to Learn
Fine-tune a model	Hugging Face Transformers, PyTorch/TF basics
Quantize for performance	`ggml`, `llama.cpp`, `GPTQ`, `bitsandbytes`
Build custom prompts	Prompt engineering, prompt templates in Ollama
Modify architecture	Learn model config files (`config.json`)
Tokenizer tuning or replacement	`tokenizers` library, vocab files

🔹 Summary Diagram

Downloaded Model

    ┌──────────────────────┐
    │   Model Weights      │  (.bin, .pt, .safetensors)
    └──────────────────────┘
    ┌──────────────────────┐
    │ Config / Architecture│  (config.json)
    └──────────────────────┘
    ┌──────────────────────┐
    │ Tokenizer Files      │  (vocab.json, merges.txt)
    └──────────────────────┘
    ┌──────────────────────┐
    │ Prompt Templates     │  (system, user format)
    └──────────────────────┘
    ┌──────────────────────┐
    │ Runner / Wrapper     │  (Modelfile, Python scripts)
    └──────────────────────┘
    ┌──────────────────────┐
    │ Quantization Metadata│  (quant-info.json, ggml meta)
    └──────────────────────┘