Introduction
🔥 unitorch is a PyTorch-based library that unifies training, inference, and serving of state-of-the-art models across NLP, computer vision, multimodal learning, and more. It wraps 20+ model architectures with a configuration-driven CLI, integrating seamlessly with transformers, peft, and diffusers.
Get started with a single import or a one-line CLI command — no boilerplate required.
Features
| Unified Model Support | 20+ architectures: LLMs, diffusion models, vision transformers, multimodal models |
| Configuration-Driven CLI | Train, evaluate, infer, and serve via .ini config files |
| Multi-GPU & Distributed | Native torchrun support + DeepSpeed integration for large-scale models |
| CUDA Optimized | Optional CUDA C++ extensions for accelerated kernels |
| PEFT / LoRA | Built-in parameter-efficient fine-tuning support |
| Model Serving | FastAPI-based serving with unitorch-fastapi |
Installation
pip install unitorch
Optional extras
pip install "unitorch[all]" # everything
pip install "unitorch[deepspeed]" # DeepSpeed support
pip install "unitorch[diffusers]" # image generation models
Requires Python >= 3.10 and PyTorch 2.5+.
Quick Start
Python API
from unitorch.models.bart import BartForGeneration
model = BartForGeneration("path/to/bart/config.json")
# Configuration-driven setup
from unitorch.cli import Config
config = Config("path/to/config.ini")
Multi-GPU Training
torchrun --no_python --nproc_per_node 4 \
unitorch-train examples/configs/generation/bart.ini \
--train_file path/to/train.tsv --dev_file path/to/dev.tsv
Inference
unitorch-infer examples/configs/generation/bart.ini --test_file path/to/test.tsv
See the documentation for full tutorials and examples.
Supported Models
View all supported models
| Domain | Models |
|---|---|
| Language | BERT, RoBERTa, XLM-RoBERTa, BART, MBart, LLaMA, Mistral, QWen3 |
| Vision | BEiT, Swin Transformer, DINOv2, CLIP, SigLIP |
| Multimodal | LLaVA, QWen3-VL, Chinese CLIP |
| Image Generation | FLUX (StableFlux), QWenImage |
| Video Generation | Wan |
| Detection | DETR, Grounding DINO |
| Segmentation | SAM, Mask2Former, SegFormer, BRIA |
| Depth Estimation | DPT |
| PEFT | LoRA, DPO, GRPO (via peft wrappers) |
CLI Commands
| Command | Purpose |
|---|---|
unitorch-train |
Train models (supports torchrun) |
unitorch-eval |
Evaluate models |
unitorch-infer |
Run batch inference |
unitorch-fastapi |
Start a FastAPI model server |
unitorch-copilot |
unitorch-native agent (similar to Claude / OpenCode) |
unitorch-copilot-cli |
CLI tool for agent use — invokes registered copilot tools |
License
Released under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for unitorch 0.0.2.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| unitorch-0.0.2.0.tar.gz | 11.8 MB | Without provenance | Details for unitorch-0.0.2.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| unitorch-0.0.2.0-py3-none-any.whl | Python 3 | none | any | Details for unitorch-0.0.2.0-py3-none-any.whl |
Total release size: 12.3 MB
Release files / unitorch-0.0.2.0.tar.gz
| Download URL | unitorch-0.0.2.0.tar.gz |
|---|---|
| Size | 11.8 MB |
| Tags | Source |
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| Download URL | unitorch-0.0.2.0-py3-none-any.whl |
|---|---|
| Size | 477.1 kB |
| Tags | Python 3 |
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