> 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/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server. # Model Coverage ## Docs - [Model Coverage Overview](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/overview.md): Hugging Face Auto class support, release history, day-0 coverage, and custom architecture registration for NeMo AutoModel. - [Model Support Log](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/release-log.md): A reverse-chronological log of model support added to NeMo AutoModel. - [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. - [Vision Language Models (VLMs)](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/overview.md): - [Cohere Labs](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/coherelabs.md): Browse NeMo AutoModel coverage for Cohere Labs vision language models. - [North-Micro-Vision-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/coherelabs/North-Micro-Vision-Instruct.md): Fine-tune North-Micro-Vision-Instruct for image-text tasks with NeMo AutoModel using the checked-in two-GPU LoRA recipe. - [DeepSeek AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/deepseek-ai.md): Browse NeMo AutoModel coverage for DeepSeek AI vision language models. - [DeepSeek-V4-Flash-Vision-Exp](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp.md): Use NeMo AutoModel to fine-tune DeepSeek-V4-Flash-Vision-Exp for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [DeepSeek-V4.1-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/deepseek-ai/DeepSeek-V4.1-Flash.md): Use NeMo AutoModel to fine-tune DeepSeek-V4.1-Flash for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Google](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google.md): Browse NeMo AutoModel coverage for Google vision language models. - [gemma-3-4b-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-3-4b-it.md): Fine-tune gemma-3-4b-it for image-text tasks with NeMo AutoModel using checked-in full-parameter, LoRA, and distributed recipes. - [gemma-3n-E4B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-3n-E4B-it.md): Fine-tune gemma-3n-E4B-it for image-text-to-text tasks with NeMo AutoModel using checked-in recipes and distributed settings. - [gemma-4-26B-A4B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-4-26B-A4B-it.md): Fine-tune gemma-4-26B-A4B-it for image-text-to-text tasks with NeMo AutoModel using checked-in recipes and distributed settings. - [gemma-4-31B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-4-31B-it.md): Fine-tune gemma-4-31B-it for image-text tasks with NeMo AutoModel using checked-in full-parameter, LoRA, tensor-parallel, and pipeline-parallel recipes. - [gemma-4-E2B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-4-E2B-it.md): Fine-tune gemma-4-E2B-it for image-text-to-text tasks with NeMo AutoModel using checked-in recipes and distributed settings. - [gemma-4-E4B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/google/gemma-4-E4B-it.md): Fine-tune gemma-4-E4B-it for image-text-to-text tasks with NeMo AutoModel using checked-in recipes and distributed settings. - [HuggingFaceTB](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/huggingfacetb.md): Browse NeMo AutoModel coverage for models published under the HuggingFaceTB Hugging Face organization. - [SmolVLM-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/huggingfacetb/SmolVLM-Instruct.md): Reference SmolVLM-Instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [LLaVA HF](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/llava-hf.md): Browse NeMo AutoModel coverage for LLaVA HF vision language models. - [llava-1.5-7b-hf](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/llava-hf/llava-1.5-7b-hf.md): Reference llava-1.5-7b-hf checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [LMMS Lab](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/lmms-lab.md): Browse NeMo AutoModel coverage for LMMS Lab vision language models. - [LLaVA-OneVision-1.5-4B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/lmms-lab/LLaVA-OneVision-1.5-4B-Instruct.md): Use LLaVA-OneVision-1.5-4B-Instruct with NeMo AutoModel for vision-language fine-tuning, including checkpoints, runnable recipes, setup, and reference details. - [LLaVA-OneVision-1.5-8B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/lmms-lab/LLaVA-OneVision-1.5-8B-Instruct.md): Use NeMo AutoModel to fine-tune LLaVA-OneVision-1.5-8B-Instruct for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Meta](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/meta.md): Browse NeMo AutoModel coverage for Meta vision language models. - [Llama-4-Scout-17B-16E-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/meta/Llama-4-Scout-17B-16E-Instruct.md): Reference Llama-4-Scout-17B-16E-Instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [Meta Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-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/vision-language-models/meta-models/Muse-Glimmer-30B.md): Use Muse-Glimmer-30B with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Muse-Spark-open-816B-A42B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/meta-models/Muse-Spark-open-816B-A42B.md): Fine-tune meta-models/Muse-Spark-open-816B-A42B with NeMo AutoModel using MedPix vision-language recipes and packed Tulu 3 context parallelism. - [MiniMax AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/minimax.md): Browse NeMo AutoModel coverage for MiniMax AI vision language models. - [MiniMax-M3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/minimax/MiniMax-M3.md): Fine-tune MiniMax-M3 text-only on Tulu-3 or with MedPix vision-language data using NeMo AutoModel supervised fine-tuning recipes. - [Mistral AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai.md): Browse NeMo AutoModel coverage for Mistral AI vision language models. - [Devstral-2-123B-Instruct-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Devstral-2-123B-Instruct-2512.md): Fine-tune Devstral-2-123B-Instruct-2512 for text generation with NeMo AutoModel using the checked-in single-node tensor-parallel LoRA recipe. - [Ministral-3-14B-Reasoning-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Ministral-3-14B-Reasoning-2512.md): Use NeMo AutoModel to fine-tune Ministral-3-14B-Reasoning-2512 for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Ministral-3-3B-Instruct-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Ministral-3-3B-Instruct-2512.md): Reference Ministral-3-3B-Instruct-2512 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [Ministral-3-8B-Reasoning-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Ministral-3-8B-Reasoning-2512.md): Use NeMo AutoModel to fine-tune Ministral-3-8B-Reasoning-2512 for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Mistral-Medium-3.5-128B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Mistral-Medium-3.5-128B.md): Use NeMo AutoModel to fine-tune Mistral-Medium-3.5-128B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Mistral-Small-4-119B-2603](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/mistralai/Mistral-Small-4-119B-2603.md): Use Mistral-Small-4-119B-2603 with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details. - [Moonshot AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/moonshotai.md): Browse NeMo AutoModel coverage for Moonshot AI vision language models. - [Kimi-K2.5](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/moonshotai/Kimi-K2.5.md): Use NeMo AutoModel to fine-tune Kimi-K2.5 for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Kimi-K3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/moonshotai/Kimi-K3.md): Use Kimi-K3 with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Kimi-VL-A3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/moonshotai/Kimi-VL-A3B-Instruct.md): Use Kimi-VL-A3B-Instruct with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA vision language models. - [NVIDIA-Nemotron-Parse-v1.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/nvidia/NVIDIA-Nemotron-Parse-v1.1.md): Use NVIDIA-Nemotron-Parse-v1.1 with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details. - [OpenGVLab](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/opengvlab.md): Browse NeMo AutoModel coverage for models published under the OpenGVLab Hugging Face organization. - [InternVL3_5-4B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/opengvlab/InternVL3_5-4B.md): Use InternVL3_5-4B with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen.md): Browse NeMo AutoModel coverage for Qwen vision language models. - [Qwen2.5-VL-3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen2.5-VL-3B-Instruct.md): Use Qwen2.5-VL-3B-Instruct with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details. - [Qwen3-VL-235B-A22B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3-VL-235B-A22B-Instruct.md): Use NeMo AutoModel to fine-tune Qwen3-VL-235B-A22B-Instruct for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3-VL-30B-A3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3-VL-30B-A3B-Instruct.md): Use NeMo AutoModel to fine-tune Qwen3-VL-30B-A3B-Instruct for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3-VL-4B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3-VL-4B-Instruct.md): Reference Qwen3-VL-4B-Instruct checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [Qwen3-VL-8B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3-VL-8B-Instruct.md): Use NeMo AutoModel to fine-tune Qwen3-VL-8B-Instruct for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.5-122B-A10B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-122B-A10B.md): Use NeMo AutoModel to fine-tune Qwen3.5-122B-A10B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.5-27B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-27B.md): Use NeMo AutoModel to fine-tune Qwen3.5-27B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.5-35B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-35B-A3B.md): Use NeMo AutoModel to fine-tune Qwen3.5-35B-A3B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.5-397B-A17B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-397B-A17B.md): Use NeMo AutoModel to fine-tune Qwen3.5-397B-A17B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.5-4B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-4B.md): Use Qwen3.5-4B with NeMo AutoModel for vision-language fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Qwen3.5-9B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.5-9B.md): Use NeMo AutoModel to fine-tune Qwen3.5-9B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.6-27B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.6-27B.md): Fine-tune Qwen3.6-27B for vision-language tasks with NeMo AutoModel using checked-in full-parameter and LoRA recipes with distributed settings. - [Qwen3.6-35B-A3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.6-35B-A3B.md): Use NeMo AutoModel to fine-tune Qwen3.6-35B-A3B for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Qwen3.8-27B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/qwen/Qwen3.8-27B.md): Fine-tune Qwen3.8-27B for vision-language tasks with NeMo AutoModel using checked-in full-parameter and LoRA recipes with distributed settings. - [StepFun AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/stepfun-ai.md): Browse NeMo AutoModel coverage for StepFun AI vision language models. - [Step-3.7-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/stepfun-ai/Step-3.7-Flash.md): Fine-tune Step-3.7-Flash on MedPix with full or LoRA vision-language recipes in NeMo AutoModel for supervised image-text training. - [Step-5-Preview](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/stepfun-ai/Step-5-Preview.md): Step-5-Preview vision-language architecture, packed context-parallel inputs, and current preprocessing scope in NeMo AutoModel. - [Thinking Machines](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/thinkingmachines.md): Browse NeMo AutoModel coverage for Thinking Machines vision language models. - [Inkling](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/thinkingmachines/Inkling.md): Fine-tune Inkling vision-language checkpoints with NeMo AutoModel using checked-in multi-node recipes with expert and pipeline parallelism. - [Inkling-Small](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/thinkingmachines/Inkling-Small.md): Use NeMo AutoModel to fine-tune Inkling-Small for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings. - [Z.ai](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/thudm.md): Browse NeMo AutoModel coverage for Z.ai vision language models. - [GLM-5.3-Flash](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/vision-language-models/thudm/GLM-5.3-Flash.md): Fine-tune GLM-5.3-Flash for vision-language tasks with NeMo AutoModel using a checked-in packed-context and expert-parallel recipe. - [Multimodal Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/multimodal/overview.md): - [ByteDance Seed](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/multimodal/bytedance-seed.md): Browse NeMo AutoModel coverage for ByteDance Seed multimodal models. - [BAGEL-7B-MoT](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/multimodal/bytedance-seed/BAGEL-7B-MoT.md): Use BAGEL-7B-MoT with NeMo AutoModel for multimodal pretraining, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Omni Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/overview.md): - [Microsoft](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/microsoft.md): Browse NeMo AutoModel coverage for Microsoft omni models. - [Phi-4-multimodal-instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/microsoft/Phi-4-multimodal-instruct.md): Use Phi-4-multimodal-instruct with NeMo AutoModel for multimodal fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA omni models. - [Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16.md): Use Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 with NeMo AutoModel for multimodal fine-tuning, including checkpoints, runnable recipes, setup, and reference details. - [NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16.md): Use NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with NeMo AutoModel for multimodal fine-tuning, with checkpoints, recipes, setup, and reference details. - [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/qwen.md): Browse NeMo AutoModel coverage for Qwen omni models. - [Qwen2.5-Omni-3B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/qwen/Qwen2.5-Omni-3B.md): Use Qwen2.5-Omni-3B with NeMo AutoModel for multimodal fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [Qwen2.5-Omni-7B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/qwen/Qwen2.5-Omni-7B.md): Use NeMo AutoModel to fine-tune Qwen2.5-Omni-7B for Omni workflows with checked-in any to any recipes and documented distributed settings. - [Qwen3-Omni-30B-A3B-Instruct](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/omni/qwen/Qwen3-Omni-30B-A3B-Instruct.md): Use Qwen3-Omni-30B-A3B-Instruct with NeMo AutoModel for multimodal fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details. - [Diffusion Language Models (dLLMs)](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/overview.md): - [Google](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/google.md): Browse NeMo AutoModel coverage for Google diffusion language models. - [diffusiongemma-26B-A4B-it](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/google/diffusiongemma-26B-A4B-it.md): Use diffusiongemma-26B-A4B-it with NeMo AutoModel for diffusion language model fine-tuning, including checkpoints, runnable recipes, setup, and reference details. - [GSAI ML](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/gsai-ml.md): Browse NeMo AutoModel coverage for GSAI ML diffusion language models. - [LLaDA-8B-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/gsai-ml/LLaDA-8B-Base.md): Fine-tune LLaDA-8B-Base as a diffusion language model with NeMo AutoModel using checked-in full-parameter and LoRA recipes. - [Inclusion AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/inclusionai.md): Browse NeMo AutoModel coverage for Inclusion AI diffusion language models. - [LLaDA2.1-mini](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/inclusionai/LLaDA2.1-mini.md): Fine-tune LLaDA2.1-mini as a mixture-of-experts diffusion language model with NeMo AutoModel using full-parameter and LoRA recipes. - [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA diffusion language models. - [Nemotron-Labs-Diffusion-8B-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/nvidia/Nemotron-Labs-Diffusion-8B-Base.md): Fine-tune Nemotron-Labs-Diffusion-8B-Base with NeMo AutoModel using checked-in full-parameter and LoRA diffusion language model recipes. - [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/qwen.md): Browse NeMo AutoModel coverage for Qwen diffusion language models. - [Qwen3-8B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/qwen/Qwen3-8B.md): Convert and fine-tune Qwen3-8B as a diffusion language model with NeMo AutoModel using the checked-in I-DLM recipe and text checkpoint. - [Z Lab](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/z-lab.md): Browse NeMo AutoModel coverage for Z Lab diffusion language models. - [Qwen3-4B-DFlash-b16](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/dllm/z-lab/Qwen3-4B-DFlash-b16.md): Train Qwen3-4B-DFlash-b16 as a speculative-decoding draft model with NeMo AutoModel using the checked-in DFlash recipe and frozen Qwen3 target. - [Diffusion Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/overview.md): - [Black Forest Labs](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/black-forest-labs.md): Browse NeMo AutoModel coverage for Black Forest Labs diffusion models. - [FLUX-3-Image-Klein-Base](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/black-forest-labs/flux-3-image-klein-base.md): Fine-tune FLUX-3-Image-Klein-Base for text-to-image generation and instruction-based image editing using NeMo AutoModel. - [FLUX.1-dev](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/black-forest-labs/FLUX.1-dev.md): Use FLUX.1-dev with NeMo AutoModel for diffusion training and fine-tuning, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [FLUX.2-dev](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/black-forest-labs/FLUX.2-dev.md): Fine-tune FLUX.2-dev for text-to-image generation with NeMo AutoModel using checked-in full-parameter and LoRA flow-matching recipes. - [Diffusers](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/diffusers.md): Browse NeMo AutoModel coverage for Diffusers diffusion models. - [LTX-2.3-Diffusers](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/diffusers/LTX-2.3-Diffusers.md): Fine-tune LTX-2.3-Diffusers for text-to-video generation with NeMo AutoModel using checked-in full-parameter and LoRA flow-matching recipes. - [HunyuanVideo Community](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/hunyuanvideo-community.md): Browse NeMo AutoModel coverage for HunyuanVideo Community diffusion models. - [HunyuanVideo-1.5-Diffusers-720p_t2v](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v.md): Use HunyuanVideo-1.5-Diffusers-720p_t2v with NeMo AutoModel for diffusion training and fine-tuning, including checkpoints, runnable recipes, setup, and reference details. - [Lightricks](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/lightricks.md): Browse NeMo AutoModel coverage for Lightricks diffusion models. - [LTX-2.3](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/lightricks/LTX-2.3.md): Fine-tune LTX-2.3 for synchronized video and audio generation with NeMo AutoModel using full-parameter, LoRA, and generation recipes. - [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/qwen.md): Browse NeMo AutoModel coverage for Qwen diffusion models. - [Qwen-Image](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/qwen/Qwen-Image.md): Train Qwen-Image text-to-image and image-editing checkpoints with NeMo AutoModel using checked-in flow-matching and generation recipes. - [Qwen-Image-2.1](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/qwen/Qwen-Image-2.1.md): Fine-tune Qwen-Image-2.1 text-to-image checkpoints with NeMo AutoModel using checked-in full-parameter and LoRA flow-matching recipes. - [Qwen-Image-Edit-2511](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/qwen/Qwen-Image-Edit-2511.md): Use NeMo AutoModel to fine-tune Qwen-Image-Edit-2511 for Diffusion workflows with checked-in image to image recipes and documented distributed settings. - [Tencent](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/tencent.md): Browse NeMo AutoModel coverage for Tencent diffusion models. - [HunyuanImage-3.0](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/tencent/HunyuanImage-3.0.md): Fine-tune tencent/HunyuanImage-3.0, an 80B MoE text-to-image transformer, with LoRA and expert parallelism in NeMo AutoModel. - [Wan AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/wan-ai.md): Browse NeMo AutoModel coverage for Wan AI diffusion models. - [Wan2.1-T2V-1.3B-Diffusers](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/wan-ai/Wan2.1-T2V-1.3B-Diffusers.md): Reference Wan2.1-T2V-1.3B-Diffusers checkpoint and architecture details for NeMo AutoModel, with setup guidance and related training recipes. - [Wan2.1-T2V-14B-Diffusers](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/wan-ai/Wan2.1-T2V-14B-Diffusers.md): Use NeMo AutoModel to fine-tune Wan2.1-T2V-14B-Diffusers for Diffusion workflows with checked-in text to video recipes and documented distributed settings. - [Wan2.2-T2V-A14B-Diffusers](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/diffusion/wan-ai/Wan2.2-T2V-A14B-Diffusers.md): Use Wan2.2-T2V-A14B-Diffusers with NeMo AutoModel for diffusion training and fine-tuning, including checkpoints, runnable recipes, setup, and reference details. - [Embedding Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/overview.md): Overview of NeMo AutoModel embedding models, including bidirectional Llama and Ministral3 backbones, Hugging Face AutoModel fallback, and bi-encoder recipes. - [Meta](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/meta.md): Browse NeMo AutoModel coverage for models published by Meta for embedding workflows. - [Llama-3.2-1B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/meta/Llama-3.2-1B.md): Use NeMo AutoModel to fine-tune Llama-3.2-1B for Embedding workflows with checked-in feature extraction recipes and documented distributed settings. - [Mistral AI](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/mistralai.md): Browse NeMo AutoModel coverage for Mistral AI embedding models. - [Ministral-3-3B-Base-2512](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/mistralai/Ministral-3-3B-Base-2512.md): Reference Ministral-3-3B-Base-2512 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA embedding models. - [llama-embed-nemotron-8b](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/nvidia/llama-embed-nemotron-8b.md): Use llama-embed-nemotron-8b with NeMo AutoModel for embedding training, with documented checkpoints, runnable recipes, setup guidance, and model reference details. - [llama-nemotron-embed-vl-1b-v2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/embedding-models/nvidia/llama-nemotron-embed-vl-1b-v2.md): Extract multimodal embeddings with llama-nemotron-embed-vl-1b-v2 in NeMo AutoModel using the checked-in bi-encoder recipe and DDP. - [Reranking Models](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/reranking-models/overview.md): Reranking model coverage in NeMo AutoModel, including optimized Llama and Qwen3 backbones, Hugging Face Auto fallback, and supported workflows. - [NVIDIA](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/reranking-models/nvidia.md): Browse NeMo AutoModel coverage for NVIDIA reranking models. - [llama-nemotron-rerank-1b-v2](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/reranking-models/nvidia/llama-nemotron-rerank-1b-v2.md): Reference llama-nemotron-rerank-1b-v2 checkpoint and architecture details for NeMo AutoModel, with model-family information, setup guidance, and upstream resources. - [Qwen](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/reranking-models/qwen.md): Browse NeMo AutoModel coverage for Qwen reranking models. - [Qwen3-Reranker-4B](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/reranking-models/qwen/Qwen3-Reranker-4B.md): Train Qwen3-Reranker-4B with NeMo AutoModel using context-aware query-document scoring, compatible checkpoint loading, and the retrieval training recipe. - [Troubleshooting](https://docs.nvidia.com/nemo/automodel/nightly/model-coverage/troubleshooting.md): Diagnose model-loading failures and checkpoint-specific training instability.