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Scalable toolkit for efficient model reinforcement
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Latest capture 2026-07-15 03:15
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pyproject.toml
python ecosystem,
0 dependencies
uv.lock
python ecosystem,
0 dependencies
3rdparty/Megatron-Bridge-workspace/pyproject.toml
python ecosystem,
0 dependencies
3rdparty/Megatron-Bridge-workspace/setup.py
python ecosystem,
0 dependencies
infra/nrl_k8s/pyproject.toml
python ecosystem,
0 dependencies
research/template_project/pyproject.toml
python ecosystem,
0 dependencies
infra/nrl_k8s/uv.lock
python ecosystem,
0 dependencies
nemo_rl/models/automodel/setup.py
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An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).
A framework for few-shot evaluation of language models.
Minimalistic large language model 3D-parallelism training
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
https://docs.nvidia.com/nemo/rl/latest/index.html
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