August 2025 monthly summary highlighting key features delivered, major fixes, and overall impact across huggingface/boomtitan and huggingface/smollm. Focused on delivering groundwork for Boom/Llama-3 integration, validation and configuration improvements, training tune-ups for Smollm3, and documentation/deployment updates that enable faster time-to-value and improved reliability.

July 2025 monthly summary for huggingface/smollm: Delivered SmolLM3 deployment configuration and public introduction, anchored by architecture and training parameter references for long-context and multi-stage training. Updated documentation and README to present SmolLM3 (3B) with performance highlights, open-source positioning, multilingual support, and dual-mode reasoning. Established groundwork for 32k-64k and 4k-32k training regimes with 8T/9T/11T tokens and advanced features such as Grouped Query Attention and NoPE. Prepared for enterprise adoption and external collaboration with clear model collection links and onboarding materials.

December 2024 highlights for huggingface/smollm: two primary deliverables drove business value and technical impact. (1) Continual pretraining scaffolding and documentation overhaul: refactored the continual-pretraining folder into pre-training, added tokenization tooling, and produced updated documentation/readmes to guide users (commits: 622d2f6c8f9548de546b34d46a849bf46444eeeb; 09751bcb24a46f0f844939e6dd8d5d5e92556637; cc583f20ea34abfd8b10392d971eea0ceda4668c). (2) Training regime enhancements and large-scale experiment configurations: introduced configurable large-scale experiments, adjusted learning-rate scheduling and step handling to enable higher-scale pretraining on finemath/openwebmath datasets, and added 60B-runs and 160B-runs (commits: 5e94da35ce0dc46f08fc78211f76692fde07a260; 947f7fdf5c5d728fb06ca3465d4ddc6bf7fd8f81; a67ed11b47ae9d19e0e4fe074a37688aa4c78837; 9a7c5032a7721e691a95430f88dd745b58f043fe).

November 2024 summary for hugingface/smollm focusing on distributed training automation and data config improvements. Delivered a SLURM-based launcher to streamline distributed runs and accompanying documentation, and refactored training data YAML to better separate dataset paths from weights and to align with nanotron main branch requirements. These changes reduce time-to-run large experiments, improve reproducibility, and lower the barrier to onboarding new contributors by standardizing setup and configuration.