base on Minimalistic large language model 3D-parallelism training <h1 align="center">⚡️ Nanotron</h1> <p align="center"> <a href="https://github.com/huggingface/nanotron/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/nanotron.svg"> </a> <a href="https://github.com/huggingface/nanotron/blob/master/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/huggingface/nanotron.svg?color=green"> </a> </p> <h4 align="center"> <p> <a href="#installation">Installation</a> • <a href="#quick-start">Quick Start</a> • <a href="#features">Features</a> • <a href="CONTRIBUTING.md">Contributing</a> <p> </h4> <h3 align="center"> <a href="https://huggingface.co/nanotron"><img style="float: middle; padding: 10px 10px 10px 10px;" width="60" height="55" src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" /></a> </h3> <h3 align="center"> <p>Pretraining models made easy </h3> Nanotron is a library for pretraining transformer models. It provides a simple and flexible API to pretrain models on custom datasets. Nanotron is designed to be easy to use, fast, and scalable. It is built with the following principles in mind: - **Simplicity**: Nanotron is designed to be easy to use. It provides a simple and flexible API to pretrain models on custom datasets. - **Performance**: Optimized for speed and scalability, Nanotron uses the latest techniques to train models faster and more efficiently. ## Installation ```bash # Requirements: Python>=3.10,<3.12 git clone https://github.com/huggingface/nanotron cd nanotron pip install --upgrade pip pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 pip install -e . # Install dependencies if you want to use the example scripts pip install datasets transformers pip install triton "flash-attn>=2.5.0" --no-build-isolation ``` > [!NOTE] > If you get `undefined symbol: ncclCommRegister` error you should install torch 2.1.2 instead: `pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cu121` > [!TIP] > We log to wandb automatically if it's installed. For that you can use `pip install wandb`. If you don't want to use wandb, you can run `wandb disabled`. ## Quick Start ### Training a tiny Llama model The following command will train a tiny Llama model on a single node with 8 GPUs. The model will be saved in the `checkpoints` directory as specified in the config file. ```bash CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node=8 run_train.py --config-file examples/config_tiny_llama.yaml ``` ### Run generation from your checkpoint ```bash torchrun --nproc_per_node=1 run_generate.py --ckpt-path checkpoints/10/ --tp 1 --pp 1 # We could set a larger TP for faster generation, and a larger PP in case of very large models. ``` ### Custom examples You can find more examples in the [`/examples`](/examples) directory: <!-- Make a table of the examples we support --> | Example | Description | | --- | --- | | `custom-dataloader` | Plug a custom dataloader to nanotron | | `datatrove` | Use the datatrove library to load data | | `doremi` | Use DoReMi to speed up training | | `mamba` | Train an example Mamba model | | `moe` | Train an example Mixture-of-Experts (MoE) model | | `mup` | Use spectral µTransfer to scale up your model | | `examples/config_tiny_llama_with_s3_upload.yaml` | For automatically uploading checkpoints to S3 | We're working on adding more examples soon! Feel free to add a PR to add your own example. 🚀 ## Features We currently support the following features: - [x] 3D parallelism (DP+TP+PP) - [x] Expert parallelism for MoEs - [x] AFAB and 1F1B schedules for PP - [x] Explicit APIs for TP and PP which enables easy debugging - [x] ZeRO-1 optimizer - [x] FP32 gradient accumulation - [x] Parameter tying/sharding - [x] Custom module checkpointing for large models - [x] Spectral µTransfer parametrization for scaling up neural networks - [x] Mamba example And we have on our roadmap: - [ ] FP8 training - [ ] ZeRO-3 optimizer (a.k.a FSDP) - [ ] `torch.compile` support - [ ] Ring attention - [ ] Interleaved 1f1b schedule ## Credits We would like to thank everyone working on LLMs, especially those sharing their work openly from which we took great inspiration: Nvidia for `Megatron-LM/apex`, Microsoft for `DeepSpeed`, HazyResearch for `flash-attn`.. ", Assign "at most 3 tags" to the expected json: {"id":"7151","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"