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I’m new to AI models and exploring how to use LLaMA for a specific task. I have a dataset with two columns, A and B, each containing 8-bit binary numbers (around 5,000 rows). My goal is to train a model that can:

Predict B when given A as input.

Perform bulk predictions by processing a file with multiple A values and outputting the corresponding B values.

(Bonus) Maybe Generate synthetic A-B pairs to expand my dataset.

I understand that LLaMA (or just LLMs in general) might not be the best-suited model for this structured binary task, but I want to approach this as an LLM learning exercise. Given that, how should I fine-tune LLaMA for this? Are there any specific preprocessing steps or training strategies that would make it work better for this use case? Any advice or resources would be greatly appreciated! I have been stuck on this for quite some time.

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Since LLaMA and LLMs in general are trained on natural language, you’ll need first to preprocess your training data into a textual format that the model understands. For example, you could format each training example as a prompt–completion pair as input A as 01010101 -> output B as 11001010. In this way you’re teaching the model a context where when it sees an 8‑bit binary number as your column A it should output the corresponding 8‑bit binary number as your column B.

Then you can do supervised fine-tuning using above preprocessed 5,000 rows as training data with the usual cross‑entropy loss for simplicity and consistency with the pre-trained LLM. Because the dataset is small, use a low learning rate such as 1e‑4 or lower and consider techniques like early stopping, or other regularization techniques, or synthetic data augmentation, all of which help to avoid overfitting.

LLMs are probabilistic, so even after fine‑tuning the model might sometimes output invalid binary strings, for example, strings that aren’t exactly 8 bits long or include unwanted tokens. Therefore you may need to employ further post‑processing logic to enforce that outputs strictly follow the 8‑bit format. Finally as an educational exercise, other models designed specifically for deterministic mappings like a simple lookup table might perform more reliably.

cinch
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