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> For a complete documentation index, see https://docs.nvidia.com/nemo-platform/llms.txt.
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# Model Catalog
Explore the model families and sizes supported by NVIDIA NeMo Customizer.
For information on setting up model entities for customization, see the [Manage Model Entities](/documentation/customizer-reference/manage-model-entities/overview) guide.
For fine-tuning and deployment tutorials, see the [Tutorials](/documentation/customizer-reference/tutorials) guide.
## Before You Start
If downloading models hosted on Hugging Face, create a secret with your Hugging Face API key, then create a FileSet and Model Entity referencing the model. See [index](/documentation/customizer-reference/manage-model-entities/overview) for setup instructions.
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## Model Families
#### [Llama Models](/documentation/customizer-reference/models/llama)
View the available Llama models from Meta, ranging from 8 billion to 70 billion parameters.
#### [Llama Nemotron Models](/documentation/customizer-reference/models/llama-nemotron)
View the available Llama Nemotron models from NVIDIA, including Nano and Super variants for efficient and advanced instruction tuning.
#### [Phi Models](/documentation/customizer-reference/models/phi)
View the available Phi models from Microsoft, designed for strong reasoning capabilities with efficient deployment.
#### [Embedding Models](/documentation/customizer-reference/models/embedding)
View the available embedding models optimized for retrieval and question-answering tasks.
#### [GPT-OSS Models](/documentation/customizer-reference/models/gpt-oss)
View the available GPT-OSS models supported for customization.
#### [Qwen Models](/documentation/customizer-reference/models/qwen)
View the available Qwen models from Alibaba Cloud, including compact variants for efficient customization.
#### [Mistral Models](/documentation/customizer-reference/models/mistral)
View the available Mistral models, including Mistral and Ministral variants for instruction-following and reasoning tasks.
## Tested Models
The following table lists models that NVIDIA tested and their available features. This is a list of *known-good* combinations, not a list of limits: NeMo Customizer can fine-tune additional Hugging Face checkpoints and regimes when their architectures are supported by the selected training backend. Compatibility varies by architecture and fine-tuning method—for example, Automodel LoRA does not support Conv1D-based models. Models and regimes outside this table may work but have not been formally validated; test them before relying on them in production.
For detailed technical specifications of each model such as architecture, parameters, and token limits, refer to the [model family](#model-families) pages.
### Large Language Models
The following models support both chat and completion model training.
| Model | Train a Chat Model with Tool Calling | Fine-tuning Options | Sequence Packing[^1] | Inference with NIM | Reasoning |
| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------ | ------------------- | -------------------------------- | ------------------------- | --------- |
| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) | Yes | Full SFT, LoRA | Yes | Supported | No |
| [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) | Yes | Full SFT, LoRA | Yes | Supported | No |
| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | Yes | Full SFT, LoRA | Yes | Supported | No |
| [nvidia/Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) | No | Full SFT, LoRA | Yes | Supported | Yes |
| [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2) | No | Full SFT, LoRA | No | Supported | Yes |
| [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) | No | Full SFT, LoRA | No | Supported (only Full SFT) | Yes |
| [nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16) | No | LoRA | No | Supported | Yes |
| [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) | No | Full SFT, LoRA | No | Supported | No |
| [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) | Yes | Full SFT, LoRA | LoRA with prompt-completion data | Supported | Yes |
| [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | No | Full SFT, LoRA | No | Supported | Yes |
| [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | No | Full SFT, LoRA | No | Supported | Yes |
| [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) | No | Full SFT, LoRA | No | Supported | No |
| [mistralai/Ministral-3-3B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512) | No | Full SFT, LoRA | No | No | No |
| [mistralai/Ministral-3-3B-Reasoning-2512](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512) | No | Full SFT, LoRA | Yes | No | Yes |
[^1]: Read more on [sequence packing with NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/latest/sft_peft/packed_sequence.html#)
### Embedding Models
| Model | Fine-tuning Options | Inference with NIM |
| --------------------------------------------------------------------------------------------- | ----------------------- | ------------------ |
| [nvidia/llama-nemotron-embed-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-1b-v2) | Full SFT, LoRA (merged) | Supported |
For detailed technical specifications and configuration information for embedding models, see the [Embedding Models](/documentation/customizer-reference/models/embedding) page.