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40 lines
2.3 KiB
Markdown
40 lines
2.3 KiB
Markdown
<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Nanotron
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[Nanotron](https://github.com/huggingface/nanotron) is a distributed training framework with tensor, parallel, and data parallelism (3D parallelism). It is designed for large-scale training workloads across hundreds of GPUs.
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Convert any Transformers model to an optimized Nanotron transformer model implementation for pretraining with the [convert_hf_to_nanotron.py](https://github.com/huggingface/nanotron/blob/main/examples/llama/convert_hf_to_nanotron.py) script.
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```bash
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torchrun --nproc_per_node=1 examples/llama/convert_hf_to_nanotron.py \
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--checkpoint_path=meta-llama/Llama-2-7b-hf \
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--save_path=./llama-7b-nanotron
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```
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## Transformers integration
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1. Load a supported Transformers model, like [`Llama`], with the [`~LlamaForCausalLM.from_pretrained`] function. This reads the `config.json` file from the checkpoint directory and creates a [`LlamaConfig`].
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2. Nanotron maps [`LlamaConfig`] to it's own config format and creates a Nanotron model.
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3. Convert Transformers weights to Nanotron. A weight mapping guides how to map Nanotron parameter names to Transformers parameter names. This includes handling transformations such as fusing the QKV projections and the gate/up projections.
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Nanotron also relies on [`AutoTokenizer`] for turning text into token ids during preprocessing and generation.
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## Resources
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- [Nanontron](https://github.com/huggingface/nanotron) repository
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- [Ultrascale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook) describes how to efficiently scale training with Nanotron |