StarCoder
product on 3 shows · 6 statements across 4 episodes · said 28 times in 7 episodes since 2023
Latent Space 19 
the MAD Podcast 8 
No Priors 1
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Latent Space 19the MAD Podcast 8No Priors 1
2025 11 mentions in 3 episodes 4 per episode
2024 10 mentions in 3 episodes 3 per episode
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6 statements about StarCoder, every show
Bachman: StarCoder-3B converted to Power Retention matches baseline loss in two hours
“After just 10,000 steps of training, which this training one took about two hours, this orange curve, you see that it fully matches the original loss.”
Bachman: PowerCoder-3B reaches 35% HumanEval accuracy versus StarCoder's 30%
“In the end, this converges to, I believe, about 35% accuracy on human eval, whereas the star coder baseline was about 30%.”
Bachman: Power Retention avoids quadratic compute scaling during long-context training
“So yeah, but we don't pay a quadratic cost. If you were looking at the star coder baseline, it would get even more, more expensive way more quickly.”
Morcos: GitHub stars do not predict code quality for model training
“Stars are not a good predictor of whether data is useful for models or not. Like, I think that's, like, the most popular repos are not necessarily higher quality, at least with respect to do they improve a model's coding capabilities.”
Liu: Sourcegraph Cody uses StarCoder 7B, matching larger models with context
“The primary model that Cody uses for inline completions right now is StarCoder seven billion. And with the benefit of context that actually matches the performance of you know, larger proprietary models.”
Liu: Cody matches GitHub Copilot completion acceptance rates using open-source StarCoder
“Like today, Cody uses StarCoder for inline completions, and with the benefit of the context that we provide, we actually show, like, comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completi…”