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---
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version: main
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family: smollm2-1.7b
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model_name:
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license: mit
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tags:
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#
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## Model Details
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- **Architecture:** SmolLM2
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```
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##
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This repository hosts multiple revisions of the model.
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To load a specific revision, use the `revision` parameter. For example:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("locuslab/
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tokenizer = AutoTokenizer.from_pretrained("locuslab/
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```
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---
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version: main
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family: smollm2-1.7b
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model_name: locuslab/safelm-1.7b_instruct_rephrase_refusal_moral_ed_600B
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license: mit
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tags:
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- model
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- transformer
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- smollm2
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- safety p
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datasets:
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- locuslab/refuseweb
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- locuslab/safeweb
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- locuslab/moral_education
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- HuggingFaceTB/smollm-corpus
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base_model:
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- locuslab/safelm-1.7b_base_rephrase_refusal_moral_ed_600B
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---
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# SafeLM-1.7B Instruct
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SafeLM is a 1.7B parameter model family that is trained via [Safety Pretraining](https://www.arxiv.org/abs/2504.16980). We train language models to be natively safe by incorporating safety
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directly into the pretraining pipeline. This is our instruction-tuned model. Our safety data curation involves scoring harmful content, rephrasing and contextualizing potentially harmful examples, and refusal training throughout pretraining.
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Please check out our [paper](https://www.arxiv.org/abs/2504.16980) and [website](https://locuslab.github.io/safety-pretraining/) for more details!
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## Model Details
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- **Architecture:** SmolLM2
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```
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## Quickstart
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("locuslab/safelm-1.7b_instruct_rephrase_refusal_moral_ed_600B")
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tokenizer = AutoTokenizer.from_pretrained("locuslab/safelm-1.7b_instruct_rephrase_refusal_moral_ed_600B")
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```
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## Citation
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If you find our work helpful, please cite our work as:
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```
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@article{maini2025safety,
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title={Safety pretraining: Toward the next generation of safe ai},
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author={Maini, Pratyush and Goyal, Sachin and Sam, Dylan and Robey, Alex and Savani, Yash and Jiang, Yiding and Zou, Andy and Lipton, Zachary C and Kolter, J Zico},
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journal={arXiv preprint arXiv:2504.16980},
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year={2025}
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}
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```
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