hugging-face-jobslisted

# Running Workloads on Hugging Face Jobs ## Overview Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. **Common use cases:** - **Data Processing** - Transform, filter, or analyze large datasets - **Batch Inference** - Run inference on thousands of samples - **Experiments & Benchmarks** - Reproducible ML experiments - **Model Training** - Fine-tune models (see `model-trainer` skill for TRL-specific training) - **Synthetic Data Generation** - Generate datasets using LLMs - **Development & Testing** - Test code without local GPU setup - **Scheduled Jobs** - Automate recurring tasks **For model training specifically:** See the `model-trainer` skill for TRL-based training workflows. ## When to Use This Skill Use this skill when users want to: - Run Python workloads on cloud infrastructure - Execute jobs without local GPU/TPU setup - Process data at scale - Run batch inference or experiments - Schedule recurring tasks - Use GPUs/TPUs for any workload - Persist results to the Hugging Face Hub ## Key Directives When assisting with jobs: 1. **ALWAYS use `hf_jobs()` MCP tool** - Submit jobs using `hf_jobs("uv", {...})` or `hf_jobs("run", {...})`. The `script` parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to `hf_jobs()`. 2. **Always handle authentication** - J