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  • Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with NeMo AutoModel
  • Function Calling with NeMo AutoModel using FunctionGemma
  • Knowledge Distillation with NeMo AutoModel
  • Fine-Tune Large MoE LLMs
  • DeepSeek V4 Flash
  • Hy3-preview
  • Pretraining Megatron Core Datasets with NeMo AutoModel
  • LLM Pre-Training with NeMo AutoModel
  • Sequence Classification (SFT/PEFT) with NeMo AutoModel
  • Fine-Tune Gemma 3 and Gemma 3n
  • Fine-Tuning Gemma 4 31B on CORD-v2 Receipts — End-to-End Guide
  • Fine-Tune Qwen3.5-VL
  • Nemotron-Omni
  • Mistral Medium 3.5 VL
  • Diffusion Model Fine-Tuning with NeMo AutoModel
  • dLLM Fine-Tuning
  • Quantization-Aware Training (QAT) in NeMo Automodel
  • Model Training on Databricks
  • Dataset Overview: LLM, VLM, and Retrieval Datasets in NeMo AutoModel
  • Integrate Your Own Text Dataset
  • Retrieval Dataset (Embedding Fine-tuning)
  • Use the ColumnMappedTextInstructionDataset
  • Use the ColumnMappedTextInstructionIterableDataset (Streaming)
  • Integrate Your Own Multi-Modal Dataset
  • Diffusion Dataset Preparation
  • Job Launchers
  • Run on Your Local Workstation
  • Run on a Cluster
  • Run with NeMo-Run
  • Run on Any Cloud with SkyPilot
  • SkyPilot k8s
  • Checkpointing in NeMo Automodel
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nemo_automodel.components.models.deepseek_v3

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Submodules

  • nemo_automodel.components.models.deepseek_v3.layers
  • nemo_automodel.components.models.deepseek_v3.model
  • nemo_automodel.components.models.deepseek_v3.rope_utils
  • nemo_automodel.components.models.deepseek_v3.state_dict_adapter
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