#1919th of 19 in Training and fine-tuning
paddlenlp
LLM training, fine-tuning, compression and inference toolkit on PaddlePaddle
- Stars
- 13k
- License
- Apache-2.0
- Last commit
- May 2026
- Last release
- Dec 2024
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
PaddleNLP is a Python toolkit built on PaddlePaddle for pretraining, fine-tuning, quantizing and serving LLMs. It supports 4D parallel training, Unified Checkpoint for elastic resume, FlashMask-based fine-tuning, and inference with FP8, INT8 and 4-bit quantization. Hardware targets include NVIDIA GPU, Kunlun XPU, Ascend NPU, Enflame GCU and Hygon DCU.
Who it is for: Teams training and deploying LLMs on PaddlePaddle, including non-NVIDIA hardware
Strengths
- Runs on NVIDIA GPU plus Kunlun XPU, Ascend NPU, Enflame GCU and Hygon DCU
- Trainer configures data, sharded, tensor and pipeline parallelism through settings
- Unified Checkpoint allows resume after cluster resizing; README claims 78.5% smaller checkpoints
- Covers Llama, Qwen, DeepSeek V3/R1, ChatGLM, Baichuan, Gemma, Mistral and others
Weaknesses
- Tied to PaddlePaddle, so it sits outside the PyTorch ecosystem
- Main README is in Chinese; English version is a separate file
- Last release was 2024-12-16, while the news list covers later model support
- No Docker or compose files detected in the repo
What it needs
- GPU optional
- Needs PaddlePaddle
- Models: Llama, Qwen, DeepSeek V2/V3/R1, ChatGLM, Baichuan
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