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

# Large Language Models

## Docs

- [Large Language Models (LLMs)](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/overview.md): 
- [Allen AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/allenai.md): Browse NeMo AutoModel coverage for Allen AI large language models.
- [OLMo-1B-hf](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/allenai/OLMo-1B-hf.md): Reference OLMo-1B-hf checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [OLMo2-7B-1124](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/allenai/OLMo2-7B-1124.md): Reference OLMo2-7B-1124 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [OLMoE-1B-7B-0924](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/allenai/OLMoE-1B-7B-0924.md): Reference OLMoE-1B-7B-0924 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [BAAI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baai.md): Browse NeMo AutoModel coverage for BAAI large language models.
- [Aquila-7B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baai/Aquila-7B.md): Reference Aquila-7B checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Baichuan Inc.](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baichuan-inc.md): Browse NeMo AutoModel coverage for Baichuan Inc. large language models.
- [Baichuan2-13B-Chat](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baichuan-inc/Baichuan2-13B-Chat.md): Reference Baichuan2-13B-Chat checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Baichuan2-7B-Chat](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baichuan-inc/Baichuan2-7B-Chat.md): Use NeMo AutoModel to fine-tune Baichuan2-7B-Chat for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Baidu](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baidu.md): Browse NeMo AutoModel coverage for Baidu large language models.
- [ERNIE-4.5-0.3B-PT](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baidu/ERNIE-4.5-0.3B-PT.md): Use ERNIE-4.5-0.3B-PT with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [ERNIE-4.5-21B-A3B-PT](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/baidu/ERNIE-4.5-21B-A3B-PT.md): Use NeMo AutoModel to fine-tune ERNIE-4.5-21B-A3B-PT for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [BigCode](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bigcode.md): Browse NeMo AutoModel coverage for BigCode large language models.
- [starcoder](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bigcode/starcoder.md): Reference starcoder checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [starcoder2-3b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bigcode/starcoder2-3b.md): Reference starcoder2-3b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [ByteDance Seed](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bytedance-seed.md): Browse NeMo AutoModel coverage for ByteDance Seed large language models.
- [Seed-Coder-8B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bytedance-seed/Seed-Coder-8B-Instruct.md): Use Seed-Coder-8B-Instruct with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Seed-OSS-36B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/bytedance-seed/Seed-OSS-36B-Instruct.md): Use NeMo AutoModel to fine-tune Seed-OSS-36B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Cohere](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/cohere.md): Browse NeMo AutoModel coverage for Cohere large language models.
- [c4ai-command-r-v01](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/cohere/c4ai-command-r-v01.md): Reference c4ai-command-r-v01 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [DeepSeek AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai.md): Browse NeMo AutoModel coverage for DeepSeek AI large language models.
- [deepseek-llm-7b-chat](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/deepseek-llm-7b-chat.md): Reference deepseek-llm-7b-chat checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [DeepSeek-V3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/DeepSeek-V3.md): Reference DeepSeek-V3 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [DeepSeek-V3.2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/DeepSeek-V3.2.md): Use NeMo AutoModel to fine-tune DeepSeek-V3.2 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [DeepSeek-V4-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/DeepSeek-V4-Flash.md): Use DeepSeek-V4-Flash with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [DeepSeek-V4-Pro](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/DeepSeek-V4-Pro.md): Fine-tune DeepSeek-V4-Pro with NeMo AutoModel using the checked-in multi-node recipe with pipeline and expert parallelism.
- [DeepSeek-V4.1-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/deepseek-ai/DeepSeek-V4.1-Flash.md): Fine-tune DeepSeek-V4.1-Flash with NeMo AutoModel using checked-in expert-parallel text recipes and sharded Engram memory.
- [EleutherAI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/eleutherai.md): Browse NeMo AutoModel coverage for EleutherAI large language models.
- [gpt-j-6b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/eleutherai/gpt-j-6b.md): Reference gpt-j-6b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [gpt-neox-20b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/eleutherai/gpt-neox-20b.md): Reference gpt-neox-20b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Google](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google.md): Browse NeMo AutoModel coverage for Google large language models.
- [functiongemma-270m-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/functiongemma-270m-it.md): Fine-tune functiongemma-270m-it for function-calling tasks with NeMo AutoModel using the checked-in text generation recipe.
- [gemma-2-9b-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/gemma-2-9b-it.md): Use gemma-2-9b-it with NeMo AutoModel for text generation, with supported architecture details, canonical checkpoints, and recipe references.
- [gemma-3-270m](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/gemma-3-270m.md): Fine-tune gemma-3-270m for text generation with NeMo AutoModel using checked-in recipes and documented distributed settings.
- [gemma-4-12B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/gemma-4-12B.md): Fine-tune gemma-4-12B for text generation with NeMo AutoModel using checked-in recipes and documented distributed settings.
- [gemma-4-31B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/gemma-4-31B.md): Fine-tune gemma-4-31B for text generation with NeMo AutoModel using checked-in recipes and documented distributed settings.
- [gemma-7b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google/gemma-7b.md): Use gemma-7b with NeMo AutoModel for text generation, with supported architecture details, canonical checkpoints, and recipe references.
- [Google T5](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google-t5.md): Browse NeMo AutoModel coverage for Google T5 large language models.
- [t5-small](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/google-t5/t5-small.md): Use t5-small with NeMo AutoModel for text-to-text generation, with supported architecture details, canonical checkpoints, and recipe references.
- [IBM](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/ibm.md): Browse NeMo AutoModel coverage for IBM large language models.
- [granite-3.0-1b-a400m-base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/ibm/granite-3.0-1b-a400m-base.md): Reference granite-3.0-1b-a400m-base checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [granite-3.0-2b-base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/ibm/granite-3.0-2b-base.md): Reference granite-3.0-2b-base checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [IBM AI Platform](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/ibm-ai-platform.md): Browse NeMo AutoModel coverage for models published under the ibm-ai-platform Hugging Face organization.
- [Bamba-9B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/ibm-ai-platform/Bamba-9B.md): Reference Bamba-9B checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Inception AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inceptionai.md): Browse NeMo AutoModel coverage for Inception AI large language models.
- [jais-13b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inceptionai/jais-13b.md): Reference jais-13b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Inclusion AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inclusionai.md): Browse NeMo AutoModel coverage for Inclusion AI large language models.
- [Ling-1T](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inclusionai/Ling-1T.md): Use NeMo AutoModel to fine-tune Ling-1T for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Ling-flash-2.0](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inclusionai/Ling-flash-2.0.md): Use NeMo AutoModel to fine-tune Ling-flash-2.0 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Ling-mini-2.0](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/inclusionai/Ling-mini-2.0.md): Use Ling-mini-2.0 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [InternLM](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/internlm.md): Browse NeMo AutoModel coverage for InternLM large language models.
- [internlm3-8b-instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/internlm/internlm3-8b-instruct.md): Reference internlm3-8b-instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [LG AI EXAONE](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/lgai-exaone.md): Browse NeMo AutoModel coverage for LG AI EXAONE large language models.
- [EXAONE-3.0-7.8B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/lgai-exaone/EXAONE-3.0-7.8B-Instruct.md): Reference EXAONE-3.0-7.8B-Instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Meta](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta.md): Browse NeMo AutoModel coverage for Meta large language models.
- [Llama-3.1-70B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta/Llama-3.1-70B.md): Use NeMo AutoModel to fine-tune Llama-3.1-70B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Llama-3.1-8B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta/Llama-3.1-8B.md): Use NeMo AutoModel to fine-tune Llama-3.1-8B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Llama-3.2-1B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta/Llama-3.2-1B.md): Use Llama-3.2-1B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Llama-3.2-3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta/Llama-3.2-3B-Instruct.md): Use NeMo AutoModel to fine-tune Llama-3.2-3B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Llama-3.3-70B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta/Llama-3.3-70B-Instruct.md): Use NeMo AutoModel to fine-tune Llama-3.3-70B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Meta Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta-models.md): Browse NeMo AutoModel coverage for models published under the meta-models Hugging Face organization.
- [Muse-Glimmer-30B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/meta-models/Muse-Glimmer-30B.md): Use NeMo AutoModel to fine-tune Muse-Glimmer-30B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Microsoft](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/microsoft.md): Browse NeMo AutoModel coverage for Microsoft large language models.
- [phi-2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/microsoft/phi-2.md): Use phi-2 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Phi-3-mini-4k-instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/microsoft/Phi-3-mini-4k-instruct.md): Use NeMo AutoModel to fine-tune Phi-3-mini-4k-instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Phi-3-small-8k-instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/microsoft/Phi-3-small-8k-instruct.md): Reference Phi-3-small-8k-instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Phi-4](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/microsoft/Phi-4.md): Use Phi-4 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [MiniMax AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/minimax.md): Browse NeMo AutoModel coverage for MiniMax AI large language models.
- [MiniMax-M2.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/minimax/MiniMax-M2.1.md): Use MiniMax-M2.1 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [MiniMax-M2.5](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/minimax/MiniMax-M2.5.md): Use NeMo AutoModel to fine-tune MiniMax-M2.5 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [MiniMax-M2.7](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/minimax/MiniMax-M2.7.md): Use NeMo AutoModel to fine-tune MiniMax-M2.7 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Mistral AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai.md): Browse NeMo AutoModel coverage for Mistral AI large language models.
- [Devstral-2-123B-Instruct-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Devstral-2-123B-Instruct-2512.md): Use NeMo AutoModel to fine-tune Devstral-2-123B-Instruct-2512 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Devstral-Small-2-24B-Instruct-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Devstral-Small-2-24B-Instruct-2512.md): Use NeMo AutoModel to fine-tune Devstral-Small-2-24B-Instruct-2512 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Ministral-3-3B-Instruct-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Ministral-3-3B-Instruct-2512.md): Use Ministral-3-3B-Instruct-2512 with NeMo AutoModel for language model fine-tuning, including checkpoints, a runnable starting command, setup, and reference details.
- [Mistral-7B-v0.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Mistral-7B-v0.1.md): Use Mistral-7B-v0.1 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Mistral-Nemo-Base-2407](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Mistral-Nemo-Base-2407.md): Use NeMo AutoModel to fine-tune Mistral-Nemo-Base-2407 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Mistral-Small-4-119B-2603](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Mistral-Small-4-119B-2603.md): Use NeMo AutoModel to fine-tune Mistral-Small-4-119B-2603 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Mixtral-8x7B-Instruct-v0.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Mixtral-8x7B-Instruct-v0.1.md): Use NeMo AutoModel to fine-tune Mixtral-8x7B-Instruct-v0.1 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Mixtral-8x7B-v0.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/mistralai/Mixtral-8x7B-v0.1.md): Use Mixtral-8x7B-v0.1 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Moonshot AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/moonshotai.md): Browse NeMo AutoModel coverage for Moonshot AI large language models.
- [Kimi-K2-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/moonshotai/Kimi-K2-Base.md): Use NeMo AutoModel to fine-tune Kimi-K2-Base for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Kimi-K3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/moonshotai/Kimi-K3.md): Fine-tune Kimi-K3 for text generation with NeMo AutoModel using checked-in multi-node recipes with expert, pipeline, and context parallelism.
- [Kimi-Linear-48B-A3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/moonshotai/Kimi-Linear-48B-A3B-Instruct.md): Use Kimi-Linear-48B-A3B-Instruct with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details.
- [Moonlight-16B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/moonshotai/Moonlight-16B-A3B.md): Fine-tune Moonlight-16B-A3B with NeMo AutoModel on 8 GPUs using FSDP2, 8-way expert parallelism, Transformer Engine, and DeepEP.
- [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA large language models.
- [Llama-3.1-Nemotron-Nano-8B-v1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/Llama-3.1-Nemotron-Nano-8B-v1.md): Use NeMo AutoModel to fine-tune Llama-3.1-Nemotron-Nano-8B-v1 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Llama-3_3-Nemotron-Super-49B-v1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/Llama-3_3-Nemotron-Super-49B-v1.md): Reference Llama-3_3-Nemotron-Super-49B-v1 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Minitron-8B-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/Minitron-8B-Base.md): Reference Minitron-8B-Base checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Nemotron-Flash-1B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/Nemotron-Flash-1B.md): Use Nemotron-Flash-1B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.md): Use NeMo AutoModel to fine-tune NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [NVIDIA-Nemotron-3-Nano-4B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16.md): Use NVIDIA-Nemotron-3-Nano-4B-BF16 with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details.
- [NVIDIA-Nemotron-3-Super-120B-A12B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.md): Use NeMo AutoModel to fine-tune NVIDIA-Nemotron-3-Super-120B-A12B-BF16 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.md): Use NeMo AutoModel to fine-tune NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.md): Use NeMo AutoModel to fine-tune NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [NVIDIA-Nemotron-Nano-9B-v2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/nvidia/NVIDIA-Nemotron-Nano-9B-v2.md): Use NeMo AutoModel to fine-tune NVIDIA-Nemotron-Nano-9B-v2 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [OpenAI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openai.md): Browse NeMo AutoModel coverage for OpenAI large language models.
- [gpt-oss-120b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openai/gpt-oss-120b.md): Fine-tune gpt-oss-120b for text generation with NeMo AutoModel using checked-in recipes and documented distributed settings.
- [gpt-oss-20b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openai/gpt-oss-20b.md): Use gpt-oss-20b with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [OpenAI Community](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openai-community.md): Browse NeMo AutoModel coverage for models published under the openai-community Hugging Face organization.
- [gpt2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openai-community/gpt2.md): Pretrain gpt2 with NeMo AutoModel using a documented checkpoint, runnable recipe, setup guidance, model reference details, and links to the pretraining guide.
- [OpenBMB](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openbmb.md): Browse NeMo AutoModel coverage for OpenBMB large language models.
- [MiniCPM5-1B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/openbmb/MiniCPM5-1B.md): Use MiniCPM5-1B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, a runnable starting command, setup guidance, and model reference details.
- [OrionStar AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/orionstar.md): Browse NeMo AutoModel coverage for OrionStar AI large language models.
- [Orion-14B-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/orionstar/Orion-14B-Base.md): Reference Orion-14B-Base checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [ParaSail AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/parasail-ai.md): Browse NeMo AutoModel coverage for ParaSail AI large language models.
- [GritLM-7B-vllm](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/parasail-ai/GritLM-7B-vllm.md): Reference GritLM-7B-vllm checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Poolside](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/poolside.md): Browse NeMo AutoModel coverage for Poolside large language models.
- [Laguna-S-2.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/poolside/Laguna-S-2.1.md): Fine-tune Laguna-S-2.1 using the NeMo AutoModel full-SFT recipe configured for two nodes, eight GPUs per node, FSDP2, and EP16.
- [Laguna-XS-2.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/poolside/Laguna-XS-2.1.md): Use NeMo AutoModel to fine-tune Laguna-XS-2.1 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen.md): Browse NeMo AutoModel coverage for Qwen large language models.
- [Qwen1.5-MoE-A2.7B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen1.5-MoE-A2.7B.md): Use Qwen1.5-MoE-A2.7B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Qwen2.5-0.5B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen2.5-0.5B.md): Use NeMo AutoModel to fine-tune Qwen2.5-0.5B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen2.5-32B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen2.5-32B-Instruct.md): Use NeMo AutoModel to fine-tune Qwen2.5-32B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen2.5-3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen2.5-3B.md): Use NeMo AutoModel to fine-tune Qwen2.5-3B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen2.5-7B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen2.5-7B-Instruct.md): Use Qwen2.5-7B-Instruct with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Qwen3-0.6B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-0.6B.md): Use Qwen3-0.6B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Qwen3-235B-A22B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-235B-A22B.md): Use NeMo AutoModel to fine-tune Qwen3-235B-A22B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen3-30B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-30B-A3B.md): Use Qwen3-30B-A3B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Qwen3-32B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-32B.md): Use NeMo AutoModel to fine-tune Qwen3-32B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen3-4B-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-4B-Base.md): Use NeMo AutoModel to fine-tune Qwen3-4B-Base for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen3-8B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-8B.md): Use NeMo AutoModel to fine-tune Qwen3-8B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen3-Next-80B-A3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3-Next-80B-A3B-Instruct.md): Use Qwen3-Next-80B-A3B-Instruct with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details.
- [Qwen3.5-35B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3.5-35B-A3B.md): Use NeMo AutoModel to fine-tune Qwen3.5-35B-A3B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Qwen3.8-2.4T-A95B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3.8-2.4T-A95B.md): Fine-tune Qwen3.8-2.4T-A95B for text generation with NeMo AutoModel using checked-in multi-node expert- and pipeline-parallel recipes.
- [Qwen3.8-Flash-Next](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/Qwen3.8-Flash-Next.md): Fine-tune Qwen3.8-Flash-Next for text generation with NeMo AutoModel using checked-in expert- and context-parallel recipes.
- [QwQ-32B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/qwen/QwQ-32B.md): Use NeMo AutoModel to fine-tune QwQ-32B for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Reflection AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/reflection.md): Browse NeMo AutoModel coverage for Reflection AI large language models.
- [Beam-1.0-501B-A23B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/reflection/Beam-1.0-501B-A23B.md): Fine-tune Beam-1.0-501B-A23B, Reflection AI's 501B sparse MoE language model, with NeMo AutoModel: a one-node LoRA recipe and an 8-node full-parameter recipe.
- [Stability AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/stabilityai.md): Browse NeMo AutoModel coverage for Stability AI large language models.
- [stablelm-3b-4e1t](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/stabilityai/stablelm-3b-4e1t.md): Reference stablelm-3b-4e1t checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [StepFun AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/stepfun-ai.md): Browse NeMo AutoModel coverage for StepFun AI large language models.
- [Step-3.5-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/stepfun-ai/Step-3.5-Flash.md): Use Step-3.5-Flash with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Step-5-Preview](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/stepfun-ai/Step-5-Preview.md): Step-5-Preview architecture, sparse attention, context parallelism, and the full-depth H100 benchmark in NeMo AutoModel.
- [Tencent](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tencent.md): Browse NeMo AutoModel coverage for Tencent large language models.
- [Hy-MT2-30B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tencent/Hy-MT2-30B-A3B.md): Use Hy-MT2-30B-A3B with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [Hy3-preview](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tencent/Hy3-preview.md): Use Hy3-preview with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [TII](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae.md): Browse NeMo AutoModel coverage for TII large language models.
- [falcon-7b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae/falcon-7b.md): Reference falcon-7b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [Falcon-H1-0.5B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae/Falcon-H1-0.5B-Instruct.md): Use NeMo AutoModel to fine-tune Falcon-H1-0.5B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Falcon-H1-1.5B-Deep-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae/Falcon-H1-1.5B-Deep-Instruct.md): Use NeMo AutoModel to fine-tune Falcon-H1-1.5B-Deep-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Falcon-H1-34B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae/Falcon-H1-34B-Instruct.md): Use NeMo AutoModel to fine-tune Falcon-H1-34B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Falcon3-7B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/tiiuae/Falcon3-7B-Instruct.md): Use NeMo AutoModel to fine-tune Falcon3-7B-Instruct for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [Upstage](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/upstage.md): Browse NeMo AutoModel coverage for Upstage large language models.
- [solar-pro-preview-instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/upstage/solar-pro-preview-instruct.md): Reference solar-pro-preview-instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [XiaomiMiMo](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/xiaomimimo.md): Browse NeMo AutoModel coverage for XiaomiMiMo large language models.
- [MiMo-V2-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/xiaomimimo/MiMo-V2-Flash.md): Fine-tune MiMo-V2-Flash and explore MiMo-V2.6-Flash-RL language and vision-language recipes, checkpoints, and settings with NeMo AutoModel.
- [MiMo-V2.5-Pro](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/xiaomimimo/MiMo-V2.5-Pro.md): Fine-tune MiMo-V2.5-Pro with NeMo AutoModel using the checked-in HellaSwag recipe for its hybrid attention MoE architecture and FP8 checkpoint.
- [MiMo-V2.6-Pro-RL](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/xiaomimimo/MiMo-V2.6-Pro-RL.md): Fine-tune MiMo-V2.6-Pro-RL with NeMo AutoModel using the checked-in GB200 recipes for packed Tulu3 text and MedPix-VQA vision-language data.
- [Z.ai](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm.md): Browse NeMo AutoModel coverage for Z.ai large language models.
- [chatglm3-6b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/chatglm3-6b.md): Reference chatglm3-6b checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources.
- [glm-4-9b-chat-hf](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/glm-4-9b-chat-hf.md): Use glm-4-9b-chat-hf with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [GLM-4.5-Air](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-4.5-Air.md): Use GLM-4.5-Air with NeMo AutoModel for language model fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details.
- [GLM-4.7](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-4.7.md): Use NeMo AutoModel to fine-tune GLM-4.7 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [GLM-5](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-5.md): Fine-tune GLM-5 through GLM-5.3 mixture-of-experts checkpoints with NeMo AutoModel using checked-in MLA and DSA recipes.
- [GLM-5.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-5.1.md): Use NeMo AutoModel to fine-tune GLM-5.1 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [GLM-5.2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-5.2.md): Use NeMo AutoModel to fine-tune GLM-5.2 for LLM workflows with checked-in text generation recipes and documented distributed settings.
- [GLM-5.3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/large-language-models/thudm/GLM-5.3.md): Use NeMo AutoModel to fine-tune GLM-5.3 for LLM workflows with checked-in text generation recipes and documented distributed settings.