What we learned fine-tuning our own coding model on a $100 budget
We're a small team at ElderAI building ATLAS Code, a coding model for agent tools (Cline, Aider, Continue, Cursor and anything else that takes an OpenAI-compatible base URL). We do our own training runs on rented GPUs with about $100 of prepaid compute. Here's an honest account of the last two days. The short version: none of our fine-tunes has cleared its quality gate yet. That's why the…
The writer at ElderAI, who is building a coding model for agent tools, discussed their experiences with fine-tuning their model on a $100 budget. They emphasized the importance of creating a gate file before analyzing results to prevent premature conclusions. The gate ensured that the fine-tune produced more accurate files than the starting checkpoint, with at most one error in standard Python coding benchmarks and at least 97% of tool calls parsing correctly.
They also learned that teaching the model to use tool calls correctly could negatively impact its coding skills if not managed properly. The team added rehearsal data and used gentler updates to prevent skill loss. They also discovered the importance of considering whitespace, tabs, and line endings when evaluating edit metrics.
The team decided to modify their scoring system to focus on unique matches and precise instructions. They found that raw tabs inside JSON strings and non-unique old_str values were common issues that slowed down the model. The writer concluded by mentioning that they had learned valuable lessons about setting up boundaries and monitoring costs within their limited budget.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
