ClimateGPT-7B-FSC
ClimateGPT is a family of AI models designed to synthesize interdisciplinary research on climate change. ClimateGPT-7B-FSC (from scratch climate) is a 7 billion parameter transformer decoder model that was pre-trained for 319.5B tokens including a collection of 4.2B tokens from curated climate documents. 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.
Model Details
Explore the model lineage here.
- Powered by: Erasmus AI
- Trained with: AppTek
- Authenticated by: EQTYLab
- Model type: decoder-only Transformer
- Language(s) (NLP): English
- License: ClimateGPT Community License
- Continued pre-trained from: Llama-2-7B
- Context length: 4K tokens
- Input: Text-only data
- Output: Model generates text only
- Paper: arXiv:2401.09646
- Website: eci.io
Uses
- This is an experimental model and it is only intended to be used to reproduce our results and for LLM research. For any other use-case, we recommend to use ClimateGPT-7B, 13B or 70B
- 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-FSC 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.
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>context
[[0]] "{reference1_title}", {reference1_year}
{reference1_text}
[[1]] "{reference2_title}", {reference2_year}
{reference2_text}
[...]<|im_end|>
<|im_start|>assistant
"""
Training
- Details on the climate-specific pre-training data are given in our paper.
- 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: 14,131 hrs
- Cloud Provider: MLFoundry
- Compute Region: Washington, USA
- Energy Mix: 100% Hydro Power (24g CO2eq/kWh according to IPCC 2014)
- Carbon Emitted: 262.8kg 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}
}