CosmoGemma_2b_en

Hey I am CosmoGemma 👋 I can answer cosmology questions from astroph.CO research articles.

This is a Gemma_2b_en fine-tuned on QA pairs (3.5k) generated from Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) from 2018-2022 and tested on QA pairs (1k) generated from 2023 articles, scoring over 75% accuracy.

All codes/data used to develop CosmoGemma can be found at the following GitHub repository: https://github.com/sultan-hassan/CosmoGemma

Example to run CosmoGemma locally:

Requirement:

keras==3.6.0
keras_nlp==0.15.1
python==3.10

If not available, install them using:

pip install -q -U keras-nlp
pip install -q -U "keras>=3"

Script:

import os

os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".                                                                                                                          
# Avoid memory fragmentation on JAX backend.                                                                                                                                                
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

import keras
import keras_nlp

gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en")
template = "Instruction:\n{instruction}\n\nResponse:\n{response}"

Question = "write your question here"

prompt = template.format(
  instruction=Question,                                                                   
  response="",
  )
out = gemma_lm.generate(prompt, max_length=1024)
ind = out.index('\n\nResponse:\n') + len('\n\nResponse:\n')
print ("Question:", Question)
print ("Answer:", out[ind:])

Training dataset

Dataset has been generated from the llama3.1:8b-instruct-fp16 model to generate QA pairs from abstracts of the Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) from 2018-2022.

Examples for some questions from the training dataset:

Question: What are some common methods for model selection in astrophysics?
Answer: The goodness of fit, the likelihood ratio test, Bayesian model selection using Bayes factors, and the classical as well as the Bayesian information theoretic approaches.

Question: What type of coupling in inflationary models can affect the prediction of inflationary parameters?
Answer: Non-minimal coupling to gravity.

Question: What type of distribution is used to model the probability of non-linear density field?
Answer: A superposition of a Gaussian and a lognormal distribution.

Question: Can the shape of central cluster galaxies be used as a predictor of weak-lensing mass bias in individual clusters?
Answer: Yes, we find that on average, the lensing masses of clusters with the roundest / most elliptical 25% of BCGs are biased ~20% high / low compared to the average.

Question: What could be the cause of remaining excess power in a signal after foreground mitigation?
Answer: Residual foreground emission from sources or diffuse emission far away from the phase centre, polarization leakage, chromatic calibration errors, ionosphere, or low-level radio-frequency interference

Question: What is the precision of photometric redshift estimates for LRGs?
Answer: 0.02

Question: What is the form of the scaling relation used to calculate X-ray luminosity?
Answer: $L_{\rm{X}} \propto \text{A}_{\rm{X}}M_{\text{200c}}^{\text{B}_{\rm{X}}} E(z)^2 (1+z)^{\gamma_{\rm{X}}}$

This is a Gemma model uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends. This model is related to a CausalLM task.

Model config:

• name: gemma_backbone
• trainable: True
• vocabulary_size: 256000
• num_layers: 18
• num_query_heads: 8
• num_key_value_heads: 1
• hidden_dim: 2048
• intermediate_dim: 32768
• head_dim: 256
• layer_norm_epsilon: 1e-06
• dropout: 0
• query_head_dim_normalize: True
• use_post_ffw_norm: False
• use_post_attention_norm: False
• final_logit_soft_cap: None
• attention_logit_soft_cap: None
• sliding_window_size: 4096
• use_sliding_window_attention: False

This model card has been generated automatically and should be completed by the model author. See Model Cards documentation for more information.