GALACTICA is a general-purpose scientific language model. It is trained on a large corpus of scientific text and data. It can perform scientific NLP tasks at a high level, as well as tasks such as citation prediction, mathematical reasoning, molecular property prediction and protein annotation. A demo is available at galactica.org.
From pip:
pip install galaiFrom repository:
pip install git+https://github.com/paperswithcode/galaiThere are five GALACTICA models available which we detail below:
| Size | Parameters |
|---|---|
mini |
125 M |
base |
1.3 B |
standard |
6.7 B |
large |
30 B |
huge |
120 B |
import galai as gal
model = gal.load_model("standard")
model.generate("Scaled dot product attention:\n\n\\[")
# Scaled dot product attention:\n\n\\[ \\displaystyle\\text{Attention}(Q,K,V)=\\text{softmax}(\\frac{QK^{T}}{\\sqrt{d_{k}}}%\n)V \\]You can find all the model weights with their model cards and inference widget in the Hugging Face Hub. All the models can be used out of the box with the transformers library.
pip install transformers accelerateYou can run inference using the high-level pipeline API
from transformers import pipeline
model = pipeline("text-generation", model="facebook/galactica-6.7b")
input_text = "The Transformer architecture [START_REF]"
model(input_text)Or for more control you can use the lower level OPTForCausalLM class. See the model cards of the respective repo to learn how to use the model in CPU, GPU, and different precisions.
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-6.7b")
model = OPTForCausalLM.from_pretrained("facebook/galactica-6.7b", device_map="auto")
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))We demonstrate some examples using the standard (6.7B) model below.
📚 Predict Citations:
model.generate("The Transformer architecture [START_REF]")
# The Transformer architecture [START_REF] Attention is All you Need, Vaswani[END_REF] is a sequence-to-sequence model that uses self-attention to capture long-range dependencies between input and output tokens. The Transformer has been shown to achieve state-of-the-art results on a wide range of natural🔢 Predict LaTeX:
model.generate("The Schwarzschild radius is defined as: \\[")
# The Schwarzschild radius is defined as: \\[r_{s}=\\frac{2GM}{c^{2}}\\]\n\nwhere \\(G\\) is the gravitational constant, \\(M\\) is the mass of the black hole, and🤔 Reasoning:
model.generate("A force of 0.6N is applied to an object, which accelerates at 3m/s. What is its mass? <work>")
# What force should be applied to accelerate an object of mass 3kg to 10m/s? <work>\nWe can use Newton's second law: F = ma. We can substitute variables to get:\n\n\\[ F = \\left(66kg📄 Generate Documents:
model.generate("Lecture 1: The Ising Model\n\n", new_doc=True, top_p=0.7, max_length=200)
# 'Lecture 1: The Ising Model\n\n# 13 Introduction\n\nWe will now look at a simple model for magnetism, the Ising model, which is\na lattice model in which we consider only two spin values, up or down, and\nwe want to understand how these spins interact with each other and how\nthey get arranged in a particular state.\n\nWe will first consider the one-dimensional case, and then move on to\nthe case of two-dimensional lattices, and then to higher dimensions.\n\n# 14 The One-Dimensional Ising Model\n\n# 14.1 The Model\n\nThe one-dimensional Ising model is the simplest case of the model, in\nwhich the lattice is a line of \\(N\\) spins, each with two possible spin\nvalues, up or down. In other words, we consider a line of \\(N\\) spins\nwhere each spin can point up or down'⚛️ Generate Molecules:
model.generate("[START_I_SMILES]", top_p=0.6, max_length=200)
# [START_I_SMILES]CCC1=CC=C(C=C1)C(=O)NC2=CC=CC(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)N[END_I_SMILES]\n\n### Molecular Formula\n\nC22H21N3O4S\n\n## Chemical and Physical Properties\n\nThe following are chemical properties for 3-[[3-(4-ethylphenyl)-3-oxo-propanoyl]amino]-N-(4-sulfamoylphenyl)benzamide.\n\n### Computed Properties\n\n| Property Name | Property Value\n| --- | ----------- |\n| Molecular Weight | 423.5\n| XLogP3-AA Log P | 3.2\n| Hydrogen Bond Donor Count | 3\n| Hydrogen Bond Acceptor Count 🧑🔬 Predict Protein Annotations:
model.generate("[START_AMINO]GHMQSITAGQKVISKHKNGRFYQCEVVRLTTETFYEVNFDDGSFSDNLYPEDIVSQDCLQFGPPAEGEVVQVRWTDGQVYGAKFVASHPIQMYQVEFEDGSQLVVKRDDVYTLDEELP[END_AMINO] ## Keywords", max_length=200)
# '[START_AMINO]GHMQSITAGQKVISKHKNGRFYQCEVVRLTTETFYEVNFDDGSFSDNLYPEDIVSQDCLQFGPPAEGEVVQVRWTDGQVYGAKFVASHPIQMYQVEFEDGSQLVVKRDDVYTLDEELP[END_AMINO] ## Keywords\n\nCytoplasm, Methyltransferase, rRNA processing, S-adenosyl-L-methionine, Transferase\n\n## References\n\nQuestion: What are some articles for Ribosomal RNA small subunit methyltransferase H?\n\nAnswer: \n\n[START_REF] Comparative Genomics of 28 Salmonella enterica Isolates: Evidence for CRISPR-Mediated Adaptive Sublineage Evolution, Fricke[END_REF]\n\n</s>'@inproceedings{GALACTICA,
title={GALACTICA: A Large Language Model for Science},
author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic},
year={2022}
}