- Pre-training code with nanotron - Evaluation suite with lighteval - Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk) - Post-training scripts with TRL & the alignment handbook - On-device tools with llama.cpp for summarization, rewriting & agents
Apache 2.0 licensed. V2 pre-training data mix coming soon!
How do I test an LLM for my unique needs? If you work in finance, law, or medicine, generic benchmarks are not enough. This blog post uses Argilla, Distilllabel and 🌤️Lighteval to generate evaluation dataset and evaluate models.