| Pros | - Fine-tunes 100+ LLMs and vision-language models from one framework, with fast day-one support for new releases
- Covers many methods: SFT, pretraining, reward modeling, PPO, DPO, KTO, ORPO, and full/freeze/LoRA/QLoRA tuning
- Zero-code option via the LLaMA Board Gradio web UI, plus a config-driven CLI
- Built-in efficiency tricks (FlashAttention-2, Unsloth, Liger Kernel) and experiment tracking (TensorBoard, W&B, MLflow, SwanLab)
- vLLM/SGLang inference with an OpenAI-style API, model export, and Apache-2.0 licensing
| - Runs fully offline on your own machine, keeping audio private
- Permissive MIT license
- Handles files, YouTube links, and live microphone transcription
- Exports to TXT, SRT, and VTT, with speaker identification and a watch-folder option
- Hardware acceleration via CUDA, Apple Silicon, and Vulkan for faster processing
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| Cons | - Requires an NVIDIA (or compatible) GPU with enough VRAM; it is not a CPU-friendly tool
- You still need to understand fine-tuning concepts (datasets, methods, hyperparameters) to get good results
- Rapidly adding new models and features can bring transformers/torch/CUDA version-compatibility friction
- Windows users must manually install a CUDA build of PyTorch before it works
| - Accuracy and speed depend on the Whisper model size and your hardware; larger models need a capable GPU
- Whisper's translation feature outputs English, so it is not a general any-to-any translator
- Installers are unsigned, so Windows and macOS show security warnings on first run
- Intel Macs are no longer supported beyond an older release; current versions require Apple Silicon
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