Why Are Model Names So Inconsistent? A Guide to Finding Models on Ollama
Z
Zack Saadioui
8/12/2025
Why Are Model Names So Inconsistent? A Guide to Finding Models on Ollama
Hey everyone, so you’ve decided to dive into the world of local AI with Ollama. That's awesome! It’s a fantastic tool that makes running powerful language models on your own machine surprisingly straightforward. But then you hit a snag, something that trips up pretty much everyone at the beginning: the model names.
You head to the Ollama library, ready to download a model, & you’re greeted with a list that looks something like this:
1
llama3.1:8b-instruct-q4_K_M
,
1
gemma2:9b-instruct-q6_K
,
1
phi3:14b-medium-4k-instruct-fp16
. It feels a bit like reading a secret code, right? And to make matters worse, the names seem to be all over the place. One model has "instruct," another has "it," some have "fp16," and others have a string of letters & numbers that look like gibberish.
Honestly, it’s a bit of a mess. And this inconsistency is one of the biggest hurdles for newcomers. You're not alone in feeling confused. In this guide, we're going to break down why Ollama model names are so inconsistent, what all those weird parts of the name actually mean, & most importantly, how to navigate this chaotic landscape to find the perfect model for your needs.
The Wild West of AI Model Naming
So, why the inconsistency? It's not like the folks behind Ollama are trying to make our lives difficult. The reality is that the world of open-source AI is a bit like the Wild West right now. Things are moving at a breakneck pace, with new models & variations popping up almost daily. This rapid, decentralized development is the main culprit behind the naming chaos.
Here’s the thing: there’s no single, universally agreed-upon naming convention for AI models. Different research teams, companies, & individual developers all have their own ways of doing things. And when these models make their way to a platform like Ollama, they bring their unique naming quirks with them.
On top of that, Ollama itself sometimes tries to simplify things, but it can occasionally backfire. For example, they might use aliases, where a simple name like
1
llama3.1
points to a more specific version like
1
llama3.1:8b-instruct-q4_K_M
. This is meant to be helpful, but it can also add another layer of confusion if you're not aware of what's happening behind the scenes.
And then there's the issue of updates. A model developer might release a new version of their model but give it the same name as the old one, just with a different tag. This can make it tricky to know if you're using the latest & greatest version. Some users on Reddit have pointed out that this can be particularly frustrating, as it's not always clear which tag corresponds to which version.
Decoding the Model Names: A Practical Glossary
Okay, so the names are inconsistent. But once you understand the different components, you can start to make sense of them. Think of it like learning a new language. At first, it's just a jumble of sounds, but once you learn the vocabulary & grammar, you can start to understand what's being said.
This is the easy part. It's simply the name of the model's developer or the family it belongs to. Some of the big names you'll see are:
Llama: Developed by Meta AI.
Gemma: Developed by Google.
Phi: Developed by Microsoft.
Mistral: Developed by Mistral AI.
Qwen: Developed by Alibaba Cloud.
Parameters
This part of the name tells you the size of the model, specifically the number of parameters it has, usually in billions (b). For example, a model with
1
7b
in its name has 7 billion parameters. A
1
70b
model has 70 billion.
Why does this matter? Well, generally speaking, more parameters mean a more capable model. It's like having a bigger brain. A 70b model will likely be better at complex reasoning tasks than a 7b model. But there's a trade-off: bigger models require more powerful hardware (more RAM & a beefier GPU) to run. So, you'll need to choose a model that's appropriate for your machine.
Type
This part of the name gives you a clue about what the model is designed for. You'll see a few common types:
Instruct or IT (Instruction-Trained): These models have been specifically fine-tuned to follow instructions. If you want to have a conversation with the model, ask it to write something for you, or give it a specific task, you'll want an "instruct" model. They're great for chatbot applications & other interactive uses.
Text or Base: These are general-purpose models. They're trained to predict the next word in a sequence. They're not as good at following instructions as "instruct" models, but they can be useful for tasks like text completion or as a base for further fine-tuning.
Chat: As the name suggests, these models are optimized for conversational AI. They're great for building chatbots & other applications where you need a natural, back-and-forth dialogue.
Quantization
This is where things get a bit more technical, but it's SUPER important. Quantization is a process that reduces the size of a model, making it easier to run on consumer hardware. It does this by reducing the precision of the model's weights. Think of it like compressing a video file. A 1080p video looks great, but it takes up a lot of space. A 480p video is smaller & easier to stream, but you lose some quality.
Quantization is a balancing act between performance & accuracy. A lower quantization level (like
1
q2
) will result in a smaller, faster model, but it might not be as accurate. A higher level (like
1
q8
) will be more accurate but will require more resources.
Here's a quick rundown of some common quantization terms:
Q4, Q5, Q6, Q8: These refer to 4-bit, 5-bit, 6-bit, & 8-bit quantization, respectively. The higher the number, the more precision is retained.
K_M, K_S, K_L: These refer to different methods of quantization, with "K" indicating a specific technique. The "M," "S," & "L" refer to the block size (medium, small, large), which affects the trade-off between precision & memory usage.
fp16 or F16: This stands for 16-bit floating-point. This is a high-precision, unquantized version of the model. It will give you the best quality, but it will also be the largest & most resource-intensive.
For most users, a
1
q4_K_M
model is a good starting point. It offers a nice balance of performance & accuracy.
A Practical Guide to Finding Your Perfect Model
Okay, now that you have a better understanding of what the names mean, how do you actually find the right model for your project? Here are a few tips:
1. Start with the Ollama Website
The Ollama website is the best place to start your search. They have a library of all the official models, & each model has its own page with a description, an example of how to run it, & a list of all the available tags.
When you're on a model's page, be sure to click the "View all" link to see all the different variations. This will give you a complete picture of all the available sizes, types, & quantization levels.
2. Use the Command Line
Once you have Ollama installed, you can use the command line to manage your models. The
1
ollama list
command will show you all the models you have downloaded. And to download a new model, you just use the
1
ollama pull
command followed by the model name.
For example, to download the 8-billion parameter, instruction-tuned, 4-bit quantized version of Llama 3.1, you would run:
1
ollama pull llama3.1:8b-instruct-q4_K_M
3. Don't Be Afraid to Experiment
Honestly, the best way to find the right model is to just try a few out. Start with a smaller, more general-purpose model to get a feel for how things work. Then, you can start to explore some of the more specialized models.
Think about what you want to use the model for. If you're building a chatbot for your business, you'll probably want an "instruct" or "chat" model. If you're doing creative writing, a "text" model might be more your speed.
And this is where a tool like Arsturn can come in handy. If you're a business looking to leverage these powerful AI models for customer service, you don't have to get bogged down in the nitty-gritty of model selection & management. Arsturn helps businesses create custom AI chatbots trained on their own data. This means you can have a chatbot that understands your specific products & services & can provide instant, accurate answers to your customers' questions, 24/7. It takes the guesswork out of choosing the right model & lets you focus on what you do best: running your business.
4. The "Latest" Tag Isn't Always the Latest
This is a big one. On the Ollama website, you'll often see a tag called "latest." You might think this means it's the newest version of the model, but that's not always the case. In fact, it usually just means it's the most popular or the default version. So, if you want to be sure you're getting the most up-to-date model, it's always a good idea to check the model's page on the Ollama website or even the developer's page on a site like Hugging Face.
The Broader Challenge: A Plea for Simplicity
The confusion around model names isn't just an Ollama problem. It's an industry-wide issue. As one article puts it, imagine if cars were named after their engine specs instead of names like "Mustang" or "Civic." That's kind of what we have in the AI world right now.
The naming conventions we have are great for researchers & developers who need to know the technical details of a model. But for the average user, they're just confusing.
There's a growing call for a more user-centric approach to AI model naming. Names should be simple, memorable, & descriptive. They should give you a clear idea of what the model does without requiring you to have a degree in computer science.
Some have suggested categorizing models by their primary function, with names like "Conversational Bot" or "Text Summarizer." While this might not be perfect for advanced models with diverse capabilities, it's a step in the right direction.
Final Thoughts
Navigating the world of Ollama model names can be a bit daunting at first, but hopefully, this guide has helped to demystify the process. Remember, the key is to understand the different components of the name & to not be afraid to experiment.
And if you're a business looking to harness the power of AI without the headache, remember that there are solutions out there like Arsturn. Building a no-code AI chatbot trained on your own data can be a game-changer for customer engagement & lead generation. It allows you to provide personalized experiences that build meaningful connections with your audience, all without having to become an expert in the arcane art of AI model naming.
The world of local AI is incredibly exciting, & Ollama is a fantastic gateway to it. So, don't let the confusing names scare you off. Dive in, start experimenting, & see what you can create.
Hope this was helpful! Let me know what you think.