Enhancing LLM Workflows with NVIDIA NeMo-Skills
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Caroline Bishop Jun 25, 2025 11:28 NVIDIA’s NeMo-Skills library offers seamless integration for improving LLM workflows, addressing challenges in synthetic data generation, model training, and evaluation. NVIDIA has introduced a new library, NeMo-Skills, aimed at simplifying the complex workflows involved in enhancing Large Language Models (LLMs). The library addresses challenges in synthetic data generation, model training, and evaluation by offering high-level abstractions that unify different frameworks, according to NVIDIA’s blog. Streamlining LLM Workflows Improving LLMs traditionally involves multiple stages, such as synthetic data generation (SDG), model training through supervised fine-tuning (SFT) or reinforcement learning (RL), and model evaluation. These stages often require different libraries, making integration cumbersome. NVIDIA’s NeMo-Skills library simplifies this process by connecting various frameworks in a unified manner, making it easier to transition from local prototyping to large-scale jobs on Slurm clusters. Implementation and Setup To leverage NeMo-Skills, users can set it up locally or on a Slurm cluster. The setup involves using Docker containers and the NVIDIA Container Toolkit for local operations. NeMo-Skills facilitates the orchestration of complex jobs by automating the upload of code and scheduling of tasks, enabling efficient workflow management. Users can establish a baseline by evaluating existing models to identify areas for improvement. The tutorial provided by NVIDIA uses the Qwen2.5 14B Instruct model and evaluates its mathematical reasoning capabilities using AIME24 and AIME25 benchmarks. Enhancing LLM Capabilities To improve the baseline, synthetic mathematical data can be generated using a small set of AoPS forum discussions. These discussions are processed to extract problems, which are then solved using the QwQ 32B model. The solutions are used to train the 14B model, enhancing its reasoning capabilities. Training can be performed using either the NeMo-Aligner or NeMo-RL backends. The library supports both supervised fine-tuning and reinforcement…
