nanotron
Enrichment pendingMinimalistic large language model 3D-parallelism training
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- As of today · Source: github_public_v1
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- As of today · Source: github_public_v1
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- No lockfile
- As of today · Source: none
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Backing
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- Company
- Hugging Face·GitHub org profile·today
- Employees
- 160·Wikidata (P1128 employees)·today
- Funding
- $235,000,000 (2023-08)·GraphCanon curated seed (public press)·today
- Commercial model
- OSS + managed cloud·GraphCanon curated seed·today
Overview
Minimalistic large language model 3D-parallelism training
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Jul 11, 2026
Categories
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
To run the code in this project, first create a Python virtual environment using e.g. `uv`:Source link
Tags
README
Installation
To run the code in this project, first create a Python virtual environment using e.g. uv:
uv venv nanotron --python 3.11 && source nanotron/bin/activate && uv pip install --upgrade pip
[!TIP] For Hugging Face cluster users, add
export UV_LINK_MODE=copyto your.bashrcto suppress cache warnings fromuv
Next, install Pytorch:
uv pip install torch --index-url https://download.pytorch.org/whl/cu124
Then install the core dependencies with:
uv pip install -e .
To run the example scripts, install the remaining dependencies as follows:
uv pip install datasets transformers datatrove[io] numba wandb