A model’s name is a specification, if you can read it

Nine fields in one string

Here is a repository identifier you will meet:

Qwen/Qwen2.5-7B-Instruct-GGUF

Unpacked, that is: owner Qwen, family Qwen2.5, roughly seven billion parameters, tuned to follow instructions, converted to the GGUF file format. Four of those five facts change what you can do with it, and all four are in the name. Learning to read the convention saves you opening twenty repositories to find out they were the wrong shape.

The convention is a habit, not a rule. Nobody validates it. But it is followed often enough that a name that breaks it is itself a signal: usually that the uploader is new, occasionally that they are hiding something.

Parameter counts, and the arithmetic they imply

7B means about seven billion parameters. 0.5B, 1.5B, 3B, 8B, 14B, 32B, 70B and 405B are the common rungs. The number is the single most decision-relevant token in the name, because it converts directly into memory:

bytes ≈ parameters × bytes per parameter

At bfloat16 that is two bytes each, so a 7B model is about 14 GB of weights. At 4-bit quantization it is roughly 3.5 to 4.5 GB. Add a gigabyte or two for the activations and the key-value cache during generation, and you can decide in your head whether a model fits a free Colab T4 (16 GB) or your laptop (often 8 GB of usable RAM) before you click anything.

Two naming patterns hide a different arithmetic:

• 8x7B is a mixture of experts. It does not mean 56 billion parameters of compute; it means roughly 47 billion parameters must be resident in memory while only about 13 billion are used per token. It is fast for its size and large for its speed. People size it as a 13B and run out of RAM.
• A3B or similar, as in Qwen3-30B-A3B, states both numbers explicitly: 30 billion total, 3 billion active. This is the honest version of the same naming problem.

Base, instruct, chat, and why it matters more than size

• No suffix, or -Base: the model was trained to continue text and nothing else. Ask it a question and a good base model will often produce three more questions, because that is what a page of questions looks like. This is not a fault.
• -Instruct, -it, -Chat: the model has been tuned to treat the input as an instruction or a conversation. It expects its own chat template, and using the wrong one degrades output in ways that look like the model being stupid rather than misused.
• -Coder, -Math, -Vision, -VL: specialised, usually at some cost to general ability.
• -Reasoning, -R1, -Thinking: trained to emit a long internal working-out before its answer. Budget for many more output tokens than you expect, because you pay for the thinking.

The practical rule: for anything you would phrase as a request, you want an instruct or chat variant. Choosing a base model by mistake is the single most common reason a beginner concludes that open models are bad.

Format and quantization suffixes

• -GGUF: converted for llama.cpp and the tools built on it, which run well on CPU and on Apple silicon. Files inside are further labelled Q4_K_M, Q5_K_S, Q8_0: the digit is bits per weight, the letters are the variant of the scheme. Q4_K_M is the common default: about a quarter of the memory of fp16 for a quality loss most people cannot detect in ordinary use, and can detect on code and arithmetic.
• -AWQ, -GPTQ: 4-bit schemes aimed at GPU serving rather than CPU.
• -ONNX: converted to a runtime that works in browsers and on ordinary CPUs.
• -bnb-4bit: pre-quantized with bitsandbytes, loadable directly by transformers.

Distil, mini, tiny, turbo

distil has a specific meaning: a smaller model trained to imitate a larger one, as in distilbert or distil-whisper. mini, tiny, small, nano and turbo have no agreed meaning at all and are chosen by whoever uploaded the file. Check the config, not the adjective.

The two things the name never tells you

Context length. A 7B model might accept 4,096 tokens or 128,000. That number lives in config.json as max_position_embeddings, and it is often the constraint that decides your project.

Licence. Nothing in the name is load-bearing here. Llama in a name does not tell you which Llama licence applies, and a fine-tune of a non-commercial model is still non-commercial no matter what its own card says.

Do this now

Take three models you have heard of and write out, from the name alone, the parameter count, whether it is instruction-tuned, and the memory it needs at bf16 and at 4-bit. Then open each config to check what you could not have known (context length) and notice how often that, not size, is the deciding number.