When I started the NanoPhi project, I had clear goals

• Using Phi-like data, textbooks

• Using a Model in the 300-400m parameter range

• Testing whether Censorship really affected a model

For My model, I chose GPT-2 Medium, a 350m Parameter model, which I finetuned and inferenced on the NanoGPT repository. It was easy to get started, as I'm familiar with the codebase, and I've made custom changes, optimizations and Bugfixes to the setup.

When It came to data, I started the way every project Replicating Phi-1.5 does: Textbooks. Two Great Starter Datasets are Nampdn's Tiny Textbooks, Along with the Sciphi dataset, and finetuning on just those two resulted in a huge performance increase. For one, it's refreshing to see the likes of GPT-2 small and medium go from simple sentence competition to textbook generators, a massive performance boost. Train and Val loss on finetuned downstream tasks were also unusually low, at around 0.71 and 0.74, possibly because the data was well structured, and very low noise.

Phi-1.5 also has other tasks, and It was quite interesting to try and Finetune a model on multitask data at the 300m scale, especially how the model still tends to cope well with 5 different varied tasks, Math, Code, Logic, Roleplay and Textbooks.

For the model, textbooks and roleplay were low-hanging fruit, as it could make textbooks and roleplay on novelty ideas, with less data compared to other categories, like asking for a textbook on "Lotuses and Ninja arts", where the textbook produced an instruction manual and QA on the usage of Lotuses by Ninja, whereas the roleplay described a moonlit pool, a dark ninja, and a precious lotus.

Code, Math, and Logic did not go so well. Initially, Logic was first seen as an emergent ability after training on textbooks gave the model basic Inductive Reasoning, like "If Beavers live in Dams and Dams are built on rivers, the Beavers must live in rivers." Further Usage of datasets like Open Platypus, did not provide much of an Improvement, but the model did start outputting small amounts of Legible Latex, and could probably produce more complex equations.

The Code parts of the NanoPhi Project are underutilized. The Model would probably be much better off if I happened to use a structured approach like the NanoPhi Paper. The current dataset simply uses ~5GB of code search net, yet this dataset is too advanced for the Level that this model is being trained for. Due to the mixture of Noisy Data and GPU constraints, the Model is weak at the basics of code and is weak at non-trivial code.

Math is affected by several reasons, including

• bad base model. GPT-2s tokenizer isn't for numbers, and has few code tokens, so it may have been a bad Idea to start from this model, but I can't pretrain on a better tokenizer like GPT-4, so I'm stuck with this one

• I may have saturated the amount of tasks the model can handle. No one has tried Teaching models of this size for multiple diverse domain tasks, and this may be the limit. However, if this were the case, then all the tasks would be worse off, but previous tasks are still performant at the same level.

• Size Difficulties. As the GPT-3 paper said "LLMs are Generalist Engines" However, I'm nowhere near that size. Math, code, and logic might just be beyond the capabilities of these model scales.

For My work, Censorship did affect model Quality. While this could just be a side effect of my data choices, after incorporating the Pippa Dataset into the Model, its other writing skills grew significantly, with the model incorporating more sensory details and having a more coherent story, more spatially accurate, better In general. You could probably Achieve similar with a curated writing dataset, perhaps those offered by Palmyra. The Pippa Data also helps make the model good at POV and Spatial Human Body Anatomy, as the positions of characters tend to make sense, with arms, heads and legs all in the right places. (perhaps those sus c.ai chats are useful after all 😉)

But the coolest thing I found, is that the model creates its own tag, a custom task, which It calls [asy]. I don't see it in the training data, but it seems to mean a mixture of code and math, and it often shows up at the ends of Code and math ans. When You prompt Code for math or use [asy] instead of [Math], the model seems to perform better.

I would expand this work towards:

• Working with this Data in longer context lens

• Observe more Multitask data, Grokking Curves, and emergent capabilities

• Pretraining on this data, or multi-epoch training

• Replicating OS phi-1 coding dataset

• Finetuning Larger Models to Increase factual knowledge and better formatting.