ibm-research/MoLFormer-XL-both-10pct
feature-extraction model by ibm-research
From the publisher
Model card & documentation
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MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets. It was introduced in the paper Large-Scale Chemical Language Representations Capture Molecular Structure and Properties by Ross et al. and first released in this repository. MoLFormer is a large-scale chemical language model designed with the intention of learning a model trained on small molecules which are represented as SMILES strings. MoLFormer leverges masked language modeling and employs a linear attention Transformer combined with rotary embeddings. An overview of the MoLFormer pipeline is seen in the image above. One can see that the transformer-based neural network model is trained on a large collection of chemical molecules represented by SMILES sequences from two public chemical datasets PubChem and ZINC in a self-supervised fashion. The MoLFormer architecture was designed with an…
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Inside the original model card — Document outline
- MoLFormer-XL-both-10%
- Model Details
- Model Description
- Intended use and limitations
- Example code
- Training Details
- Data
- Hardware
- Evaluation
- Citation
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Model overview
ibm-research/MoLFormer-XL-both-10pct is a feature-extraction model repository published by ibm-research on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- transformers
- Recent downloads (30 days)
- 231,114
- Cumulative likes
- 43
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 46,805,760
- Architecture
- MolformerForMaskedLM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2023-10-20T20:14:50.000Z
- Last modified
- 2026-07-23T14:28:52.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
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