I wanted to start a broader setup showcase thread around Llama Models.
A useful angle is data quality, but it also connects with setup choices. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Llama Models is genuinely useful and where it still needs improvement.
A useful angle is data quality, but it also connects with setup choices. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Llama Models is genuinely useful and where it still needs improvement.
