Exploring Microsoft's Phi-3-Mini and its integration with tool like Ollama | Hacker News Reader

Exploring Microsoft's Phi-3-Mini and its integration with tool like Ollama

pieces.app

84 pointsNik091231 comments

Phi-3 is five months old now. I suggest trying Phi 3.5 instead - it's effectively the same size (2.2GB from HF - Phi-3 Mini is 2.2GB as well) but should provide better results.

If you have Ollama installed for that there are plenty of other interesting models to try out too. I like the Llama 3.2 small models, or if you have a whole lot of RAM (I use 64GB on an M2 MacBook Pro) you can run Llama 3.3 70B which is genuinely GPT-4 class: https://simonwillison.net/2024/Dec/9/llama-33-70b/

How does Llama-3.2 3B compare to Phi-3.5?

This is the AI I am excited for. Data and execution local to my machine. I think Intel is betting on this with the copilot included processors. I hope ollama or other local AI services will be able to utilize these co-processors soon.

The NPUs on laptops don't have access to enough memory to run very large models.

Oftentimes they do. If they don't, it's not very hard to page memory to and from the NPU until the operation is completed.

The bigger problem is that this NPU hardware isn't built around scaling to larger models. It's laser-focused on dense computation and low-precision inference, which usually isn't much more efficient than running the same matmul as a compute shader. For Whisper-scale models that don't require insanely high precision or super sparse decoding, NPU hardware can work great. For LLMs it is almost always going to be slower than a well-tuned GPU.

Right, but for most people do they need access to a huge model locally?

Most people shouldn't host locally at all.

Of those who do, I can see students and researchers benefiting from small models. Students in particular are famously short on money for fancy hardware.

My experience trying one of the Phi models (I think 3, might have been 2) was brief, because it failed so hard: my first test was to ask for a single page web app Tetris clone, and not only was the first half the output simply doing that task wrong, the second half was a sudden sharp turn into python code to train an ML model — it didn't even delimit the transition, one line javascript, the next python.

> My experience trying one of the Phi models (I think 3, might have been 2) was brief

The Phi models are tiny LMs, maybe SLM is more fitting label than LLM (Large -> Small). As such, you cannot throw even semi-complicated problems at them. Things like "autocomplete" and other simpler things are the use cases you'd use it for, not "code this game for me", you'll need something much more powerful for that.

> Things like "autocomplete" and other simpler things are the use cases you'd use it for, not "code this game for me", you'll need something much more powerful for that.

Indeed, clearly.

However, it was tuned for chat, and people kept telling me it was competitive with the OpenAI models for coding.

Asking a leading LLM to "code a game" is a tall order. I have found a lot of success with self hosted small models to accomplish coding that would have taking me months without. I just break down the "code me a game" to its parts.

Think of it like an extended auto complete.

AFAIU NPUs are for things like voice input/output, computer vision/hand gesture io, knowing how many people/who are in front of the camera etc. Always on, real-time "ai peripherals" - not content generation.

I believe Microsoft calls them "SLMs - Small Language Models".

Maybe a better solution is privately hosted cloud solution, or just any SaaS that cannot violate data privacy by design.

> any SaaS that cannot violate data privacy by design

And that is hosted in a jurisdiction that forces them to take it seriously, e.g. Mistral in France that has to comply with GDPR and any AI and privacy regulations out of the EU.

in my opinion there is room for small and fast and large and slow but much smarter models. Use cases like phone keyboard autocomplete and next few words suggestion in coding or writing need very fast models that should by definition small. Very large models that are much smarter are also useful, for instance debugging issues or proofreading long letters.

Cursor really aced this. The Cursor model is very fast to suggest useful inline completions and then leaves big problems to big models.

Could chaining models together via tool calls based on benchmarking that would redirect to the best model allow for smaller models to perform as well as big[er] models for memory constrained/local environments?

Yes, indeed, see for example https://arxiv.org/abs/2310.03094

Are there any other tools similar to pieces.app that are useful and preferably open-source, which can be integrated into the developer workflow? I’ve used Heynote, which helps to some extent, but it’s not a direct fit and isn’t a complete AI developer workflow companion.

Is there any rule of thumb for small language models vs large language models? I've seen phi 4 called a small language model but with 14 billion parameters, it's larger than some large language models.

7b to 9b is usually what we call small. the rule of thumb is a model that you can run on a single GPU.

It’s not a useful distinction. The first LLMs had less than 1 billion parameters anyway.

I would claim that even 500 million parameters could be considered large.

Apologies for the dumb question - can these models be used at my work, i.e, for commercial purposes? What is the legality of it?

In the USA, code generated by a computer cannot be copyrighted. So you can use it for commercial purposes, but you can't control it the way you could with code that you wrote yourself. And that's legally fine, but your company's legal department might not like that idea.

But this model can be used for more than generating code, no?

That's not entirely accurate. In the US, computer-generated code can be copyrighted. The key point is that copyright protection extends to the original expression in the code, but not to its functional aspects, such as algorithms, system design, or logic.

This person has no idea what they're talking about. "Code generated by a computer"?

"works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author"

https://arstechnica.com/information-technology/2023/02/us-co...

Phi-3-mini has a MIT license, which is commercial friendly: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct

Do we know for sure that model is not trained with copyrighted material or with GPL-lisenced code? That is the biggest issue right now.

That is the case with every LLM (except a couple research experiments) and won’t be resolved until the courts do.

Literally every tech company that uses LLMs would be in legal trouble if that becomes the precedent.

Yes. It is a bigger problem than the correct lisence of the model, and I feel that original commenter is not aware of that.

Many companies are waiting for court decisions and are not using even GitHub Copilot. There is even growing business for making analysis for binaries and source code whether they use GPL code or not.

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