> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Recipes & E2E Examples

## Docs

- [Recipes and End-to-End Examples](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/overview.md): 
- [Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/sft-peft.md): 
- [Function Calling with NeMo AutoModel using FunctionGemma](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/function-calling.md): 
- [Multi-Turn Agent (Tool-Calling) SFT with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/agent-sft.md): End-to-end multi-turn, function-calling SFT on Qwen2.5-3B with the glaive_toolcall dataset
- [Knowledge Distillation with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/knowledge-distillation.md): 
- [Fine-Tune Large MoE LLMs](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/large-moe-fine-tuning.md): 
- [DeepSeek V4 Flash](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/deepseek-v4-flash.md): 
- [Hy3-preview](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/hy3-preview.md): 
- [Fine-Tune Nemotron-3-Ultra-550B](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/nemotron-3-ultra.md): End-to-end LoRA PEFT fine-tuning of Nemotron-3-Ultra-550B on HellaSwag using NeMo AutoModel on GB200 and H100.
- [Pretrain Megatron Core Datasets with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/pretraining.md): 
- [LLM Pre-Training with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/nanogpt-pretraining.md): 
- [Sequence Classification (SFT/PEFT) with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/sequence-classification.md): 
- [Retrieval Fine-Tuning (Bi-Encoder and Cross-Encoder)](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/retrieval-finetune.md): Fine-tune bi-encoder and cross-encoder retrieval models in NeMo AutoModel: data prep, configs, distributed launch, LoRA, hard-negative mining, and evaluation.
- [Fine-Tune Gemma 3 and Gemma 3n](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/gemma-3-3n.md): 
- [Fine-Tune Gemma 4 31B on CORD-v2 Receipts — End-to-End Guide](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/gemma-4.md): 
- [Fine-Tune Qwen3.5-VL](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/qwen3-5-vl.md): 
- [Nemotron-Omni](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/nemotron-omni.md): 
- [Mistral Medium 3.5 VL](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/mistral-medium-3-5.md): 
- [MiniMax-M3](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/minimax-m3.md)
- [Fine-Tune Step-3.7-Flash](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/step-3-7.md): End-to-end SFT and LoRA setup for Stepfun AI's Step-3.7-Flash 198B-A13B MoE vision-language model.
- [Fine-Tune Qwen3-Omni for ASR](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/qwen3-omni-asr.md): End-to-end ASR fine-tuning of Qwen3-Omni-30B on Hugging Face audio datasets with NeMo AutoModel.
- [Diffusion Model Fine-Tuning with NeMo AutoModel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/diffusion-fine-tuning.md): 
- [Fine-Tuning DiffusionGemma](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/diffusiongemma.md)
- [Train an EAGLE Drafter for Speculative Decoding — End-to-End Guide](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/eagle-speculative-decoding.md): Step-by-step guide for training EAGLE-3 and EAGLE-1 speculative decoding drafters with NeMo AutoModel, from data preparation through SGLang serving.
- [Train a DSpark Drafter for Speculative Decoding](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/dspark-speculative-decoding.md): Train a DSpark semi-autoregressive speculative-decoding drafter with NeMo AutoModel, from data through 2-GPU FSDP2 training.
- [dLLM Fine-Tuning](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/dllm-fine-tuning.md): 
- [Quantization-Aware Training (QAT) in NeMo Automodel](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/qat.md): 
- [Model Training on Databricks](https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/databricks.md):