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  • Home
  • About NeMo AutoModel
  • Key Features and Concepts
  • Install NeMo AutoModel
  • YAML Configuration
  • 🤗 Transformers API Compatibility
  • Repository Structure
  • Announcements
  • Performance Summary
  • Model Coverage Overview
  • Model Release Log
  • Overview
  • Llama
  • Gemma
  • Qwen2
  • Qwen2 MoE
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  • Recipes and End-to-End Examples
  • 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
  • Gradient (Activation) Checkpointing in NeMo AutoModel
  • Pipeline Parallelism with AutoPipeline
  • FP8 Training in NeMo AutoModel
  • MLflow Logging in NeMo AutoModel
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nemo_automodel.components.models.baichuan

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  • nemo_automodel.components.models.baichuan.configuration
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