ClimateGPT is a family of AI models designed to synthesize interdisciplinary research on climate change.
ClimateGPT-7B is a 7 billion parameter transformer decoder model that was adapted from Llama-2 to the domain of climate science using continuous pre-training on a collection of 4.2B tokens from curated climate documents created by Erasmus AI.
The model is further instruction fine-tuned on a dataset of instruction-completion pairs manually collected by AppTek in cooperation with climate scientists.
ClimateGPT-7B outperforms Llama-2-70B Chat on our climate-specific benchmarks.
The model is designed to be used together with retrieval augmentation to extend the knowledge, and increase the factuality of the model and with cascaded machine translation to increase the language coverage.
This model is intended to be directly used as a question answering model that is specialized in the climate domain.
The model is aimed at providing useful feedback for decision makers, scientists and journalists involved in climate discussions.
The model can also be used as a starting point for interested developers for further fine-tuning.
The model is NOT intended to be a general-purpose chatbot (although it has chat capabilities).
For the full system including cascaded MT, RAG, etc., we recommend the user to go to our demo website: eci.io
Despite the efforts from the development team to eliminate them, as every other chat-capable LLMs, this model may generate biased, offensive or inaccurate responses.
Downstream Use
ClimateGPT-7B is an instruction-tuned model that can be directly used for climate-specific question-answering applications.
It was trained to perform well with retrieval augmentation and supports up to 5 references in context.
The model was trained using ChatML so the following format should be followed when prompting, including the <|im_start|>, <|im_end|> tags, system, user, context and assistant identifiers and [[0]], [[1]]] etc. tokens to indicate references.
For continued pre-training, 4.2B climate-specific tokens (tokenized by the Llama tokenizer) are used.
For instruction fine-tuning, about 272K instruction-completion pairs (both in the climate domain but also general domain) are used.
Evaluation
Detailed evaluation results are presented in our paper on our model card website: eci.io/model-card
Environmental Impact
Hardware Type: 8x NVIDIA H100 HBM
Power Consumption per GPU: 775W
Hours used: 157 hrs
Cloud Provider: MLFoundry
Compute Region: Washington, USA
Energy Mix: 100% Hydro Power (24g CO2eq/kWh according to IPCC 2014)
Carbon Emitted: 2.9kg CO2eq
Citation
If you find ClimateGPT is useful in your work, please cite it with:
@misc{thulke2024climategpt,
title={ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change},
author={David Thulke and Yingbo Gao and Petrus Pelser and Rein Brune and Rricha Jalota and Floris Fok and Michael Ramos and Ian van Wyk and Abdallah Nasir and Hayden Goldstein and Taylor Tragemann and Katie Nguyen and Ariana Fowler and Andrew Stanco and Jon Gabriel and Jordan Taylor and Dean Moro and Evgenii Tsymbalov and Juliette de Waal and Evgeny Matusov and Mudar Yaghi and Mohammad Shihadah and Hermann Ney and Christian Dugast and Jonathan Dotan and Daniel Erasmus},
year={2024},
eprint={2401.09646},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
