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Merge branch 'main' into akoumpa/feat_custom_lora_backward
Conflicts resolved:
- checkpoint/checkpointing.py: main moved _is_geq_torch_2_9 / CheckpointingConfig
/ SaveConsolidatedMode into checkpoint/config.py and added dtype-mapping
normalization; kept main's import-based structure and preserved the
nemotron-singlegpu-lora module NOTE. Dropped the now-redundant local
_is_geq_torch_2_9 definition.
- models/nemotron_v3/model.py: kept both independent method sets — this branch's
gradient_checkpointing_enable/disable and main's PP-stage helpers (#2316).
- examples/llm_finetune/gpt_oss/gpt_oss_20b_single_gpu.yaml: kept deleted (this
branch removed it together with its test-config references; nothing in the
merged tree references it — only the _peft variant remains).
Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
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## 📣 News and Discussions
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-[06/04/2026][**Nemotron-3 Ultra**](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16) We now support finetuning NVIDIA's Nemotron 3 Ultra 550B A55B. Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/nemotron/nemotron_ultra_v3_hellaswag_peft.yaml) and [guide](https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/guides/llm/nemotron-3-ultra.md).
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-[06/03/2026][**Gemma 4 12B**](https://huggingface.co/google/gemma-4-12B) We now support finetuning the dense `google/gemma-4-12B` model. Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/gemma/gemma_4_12b_hellaswag.yaml).
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-[05/27/2026][**Step-3.7-Flash**](https://huggingface.co/stepfun-ai/Step-3.7-Flash) We added model coverage for Stepfun AI's 198B-A13B MoE vision-language model, targeting image/video agentic developer workflows with a 256k context language backbone and 1.8B ViT vision tower. See the [model coverage page](https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/model-coverage/vlm/stepfun-ai/step-3-7.md).
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-[05/19/2026][**Ling 2.0**](https://huggingface.co/collections/inclusionAI/ling-20) We now support finetuning the inclusionAI Ling 2.0 MoE family (`inclusionAI/Ling-mini-2.0`, `inclusionAI/Ling-flash-2.0`, and `inclusionAI/Ling-1T`), thanks to [@Hayden727](https://github.com/Hayden727). Check out our [recipes](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/llm_finetune/ling).
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-[05/17/2026][**ERNIE 4.5**](https://huggingface.co/baidu) and [**MiMo-V2-Flash**](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) We now support finetuning `baidu/ERNIE-4.5-0.3B-PT`, `baidu/ERNIE-4.5-21B-A3B-PT`, and `XiaomiMiMo/MiMo-V2-Flash`. Check out our ERNIE [dense recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/ernie4_5/ernie4_5_0p3b_hellaswag.yaml), ERNIE [MoE recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/ernie4_5/ernie4_5_21b_a3b_hellaswag.yaml), and MiMo [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/mimo_v2_flash/mimo_v2_flash_hellaswag.yaml).
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