Results for “multi-node-training”
16 skills
jiachen-t-wang emu-generative-pretraining-in-multimodality-arxiv-2307-05222
Emu: Generative Pretraining in Multimodality
6
orchestra-research openrlhf-training
Train large language models (7B-70B+) with RLHF using PPO, GRPO, DPO, and other algorithms, accelerated by Ray and vLLM for distributed multi-GPU setups.
10.4k · bundle
jiachen-t-wang eva-clip-improved-training-techniques-for-clip-at-scale-arxi
EVA-CLIP: Improved Training Techniques for CLIP at Scale
6
jiachen-t-wang chameleon-mixed-modal-early-fusion-foundation-models-arxiv-2
Chameleon: Mixed-Modal Early-Fusion Foundation Models
6
jiachen-t-wang multimodal-few-shot-learning-with-frozen-language-models-arx
Multimodal Few-Shot Learning with Frozen Language Models
6
jiachen-t-wang visual-instruction-tuning-arxiv-2304-08485v2
Visual Instruction Tuning
6
jiachen-t-wang matryoshka-representation-learning-arxiv-2205-13147v4
Matryoshka Representation Learning
6
dracounion project-based-learning
当需要快速掌握一门新技能或知识领域，且希望通过实践而非单纯理论学习时
11 · bundle
jiachen-t-wang gemini-a-family-of-highly-capable-multimodal-models-arxiv-23
Gemini: A Family of Highly Capable Multimodal Models
6
jiachen-t-wang multimodal-learning-with-transformers-a-survey-arxiv-2206-06
Multimodal Learning with Transformers: A Survey
6
jiachen-t-wang multimodal-neurons-in-artificial-neural-networks-arxiv-2103-
Multimodal Neurons in Artificial Neural Networks
6
qcmuu openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
0 · bundle
tianhao909 openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
1 · bundle
jiachen-t-wang mixup-beyond-empirical-risk-minimization-arxiv-1710-09412v2
Mixup: Beyond Empirical Risk Minimization
6
nvidia mcore-run-on-slurm
Launch distributed Megatron-LM training jobs on a SLURM cluster with a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules, container conventions, monitoring, and per-rank failure diagnosis.
2.2k · bundle
k-dense-ai pytorch-lightning
Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.
30.2k · bundle