| SFT (full fine-tune) | You need maximum accuracy and have the GPU budget to update all weights | Instruction / chat dataset | LLM | Start fine-tuning |
| PEFT (LoRA) | You want to fine-tune on limited GPU memory; updates <1 % of parameters | Instruction / chat dataset | LLM | Start LoRA |
| Tool / function calling | Your model needs to call APIs or tools with structured arguments | Function-calling dataset (queries + tool schemas) | LLM | Add tool calling |
| Fine-tune VLM | Your task involves both images and text (e.g., visual QA, captioning) | Image + text dataset | VLM | Fine-tune VLM |
| Fine-tune Gemma 4 | You want to fine-tune Gemma 4 for structured extraction from images (e.g., receipts) | Image + text dataset | VLM | Fine-tune Gemma 4 |
| Fine-tune dLLM | You want to fine-tune a diffusion language model (e.g., LLaDA) using masked denoising | Instruction / chat dataset | dLLM | Fine-tune dLLM |
| Fine-tune Diffusion | You want to fine-tune a diffusion model for image or video generation | Video / Image dataset | Diffusion | Fine-tune Diffusion |
| Fine-tune VLM-MoE | You need large-scale vision-language training with sparse MoE efficiency | Image + text dataset | VLM (MoE) | Fine-tune VLM-MoE |
| Fine-tune agentic VLM-MoE | You need image/video context for agentic developer workflows | Image / video + text dataset | VLM (MoE) | Fine-tune Step-3.7-Flash |
| Fine-tune Audio ASR | Adapt Qwen3-Omni for speech recognition on HF audio datasets | Audio + transcript dataset | Qwen3-Omni | Fine-tune Qwen3-Omni ASR |
| Embedding fine-tune | You want to improve text similarity for search, retrieval, or RAG | Text pairs / retrieval corpus | LLM | Train a bi-encoder or cross-encoder |
| Fine-tune a large MoE | You are adapting a large sparse MoE model (DeepSeek-V3, GLM-5, etc.) to your domain | Text dataset (e.g., HellaSwag) | LLM (MoE) | Fine-tune MoE |
| Fine-tune DeepSeek-V4 Flash | You want to fine-tune the DeepSeek-V4 Flash hybrid-attention MoE (SWA / CSA / HCA + hash-routing) | Text dataset (e.g., HellaSwag) | LLM (MoE) | Fine-tune DeepSeek-V4 Flash |
| Fine-tune Hy3-preview | You want to fine-tune Tencent’s 295B MoE with sigmoid routing and per-head QK RMSNorm | Text dataset (e.g., HellaSwag) | LLM (MoE) | Fine-tune Hy3-preview |
| Fine-tune Nemotron-3 Ultra | You want to fine-tune NVIDIA’s 550B-A55B hybrid Mamba-2 / LatentMoE model with MTP | Text dataset (e.g., HellaSwag) | LLM (MoE) | Fine-tune Nemotron-3 Ultra |
| Sequence classification | You need to classify text into categories (sentiment, topic, NLI) | Text + labels (e.g., GLUE MRPC) | LLM | Train classifier |
| QAT fine-tune | You want a quantized model that keeps accuracy for efficient deployment | Text dataset | LLM | Enable QAT |
| Knowledge distillation | You want a smaller, faster model that retains most of the teacher’s quality | Instruction dataset + teacher model | LLM | Distill a model |
| Pretrain an LLM | You are building a base model from scratch on your own corpus | Large unlabeled text corpus (e.g., FineWeb-Edu) | LLM | Start pretraining |
| Pretrain (NanoGPT) | You want quick pretraining experiments on a single node | FineWeb / text corpus | LLM | Try NanoGPT |
