Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

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chemlactica-1.3b - AWQ
- Model creator: https://huggingface.co./yerevann/
- Original model: https://huggingface.co./yerevann/chemlactica-1.3b/




Original model description:
---
license: cc-by-nc-4.0
language:
- en
library_name: transformers
tags:
- chemistry
- biology
---
Chemlactica-1.3B is a continually pretrained [galactica-1.3b](https://huggingface.co./facebook/galactica-1.3b) model for organic molecules. 
It is pretrained on [40B tokens covering 110M+ molecules from PubChem](https://huggingface.co./datasets/yerevann/PubChemForLM) as well as their chemical properties 
(molecular weight, synthetic accessibility score, drug-likeness etc.) 
and similarities (Tanimoto distance between ECFP fingerprints).

Example prompts:

`</s>[START_SMILES]CC(=O)OC1=CC=CC=C1C(=O)O[END_SMILES][SAS]` will attempt to predict the synthetic accessibility score of the given molecule.

`</s>[SAS]2.25[/SAS][SIMILAR]0.62 CC(=O)OC1=CC=CC=C1C(=O)O[/SIMILAR][START_SMILES]` will attempt to generate a molecule that has 2.25 SAS score and 
has a 0.62 similarity score to the given molecule.

The model can be wrapped into an optimization loop to traverse the chemical space with evolving prompts. See the [code on GitHub](https://github.com/YerevaNN/ChemLactica). 

A preprint with the details of the model and an optimization algorithm built on top of this model that sets state-of-the-art on 
Practical Molecular Optimization and other benchmarks is [available on arxiv](https://arxiv.org/abs/2407.18897).

Few notes:
* All queries should start with `</s>` symbol.
* All numbers are rounded to two decimal points.
* All SMILES are canonicalized using `rdkit`.
* Available tags: `[CLOGP]`, `[WEIGHT]`, `[QED]`, `[SAS]`, `[TPSA]`, `[RINGCOUNT]`, `[SIMILAR]`...

The model is part of the 3-model family: [Chemlactica-125M](https://huggingface.co./yerevann/chemlactica-125m), 
[Chemlactica-1.3B](https://huggingface.co./yerevann/chemlactica-1.3b) and [Chemma-2B](https://huggingface.co./yerevann/chemma-2b).

We are looking forward to see the community using the model in new applications and contexts.