Erasmus-AI

climategpt-3-8b

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README

License: apache-2.0

Model details

Base model: Qwen/Qwen3-8B
Model type: Causal LM
Language(s): English
Context length: 8192 tokens (SFT configuration)
License: Apache-2.0
Release artifact: Fully merged weights (standalone model; no adapter required)

Intended use

Climate and sustainability Q&A
Planetary Boundaries–focused education and analysis
Drafting and summarization of climate-related content

Limitations

The model may produce incorrect or outdated information.
Training data is largely English web content; this can introduce geographic/cultural and topical biases.
The model is not a substitute for professional scientific, medical, legal, or policy advice.

Training

ClimateGPT-3-8B was built in multiple stages:

Continued pretraining (CPT)

Starting from Qwen/Qwen3-8B, we performed continued pretraining on climate-focused corpora primarily derived from FineWeb-Edu (SmolLM-Corpus) using climate- and Planetary Boundaries–oriented filtering.

The data selection emphasizes climate science and Planetary Boundaries terminology and includes filtering to reduce off-topic matches from ambiguous terms.

Supervised fine-tuning (SFT)

We performed supervised fine-tuning using a mixture of:

Climate instruction-following data
Multi-turn conversations
Safety/refusal examples
Tool-use data
Synthetic climate / Planetary Boundaries Q&A

Training data

Public data

FineWeb-Edu (via HuggingFaceTB/smollm-corpus)
- Used for climate- and Planetary Boundaries–filtered continued pretraining.
- Dataset license: ODC-By
- Dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus

Non-public / generated data

In addition to public data, the training mix includes internal and/or generated instruction data. These datasets are not redistributed with this model.

Evaluation

We evaluate climate-domain performance using a Planetary Boundaries evaluation suite compatible with EleutherAI’s lm-evaluation-harness.

A representative comparison (from this project’s Planetary Boundaries evaluation artifacts) between a ClimateGPT 8B checkpoint and the base Qwen3-8B:

Table with columns: Task, Metric, ClimateGPT, Qwen3-8B
Task	Metric	ClimateGPT	Qwen3-8B
`planetary_boundaries_mcq_large`	acc	0.4422	0.3533
`planetary_boundaries_mcq_large`	acc_norm	0.4278	0.3900
`planetary_boundaries_mcq_hard`	acc	0.3467	0.2711

How to use

Transformers

This repository contains a standalone model. You can load it directly with Transformers.

python

from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Erasmus-AI/climategpt-3-8b" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") prompt = "Explain the Planetary Boundaries framework in simple terms."inputs = tokenizer(prompt, return_tensors="pt").to(model.device) out = model.generate(    **inputs,    max_new_tokens=512,    do_sample=True,    temperature=0.6,    top_p=0.95,)print(tokenizer.decode(out[0], skip_special_tokens=True))

vLLM

This model is intended to be compatible with vLLM.

License

Model weights: Apache-2.0
Base model: Qwen/Qwen3-8B (Apache-2.0)

Attribution

If you use this model, please cite/attribute the upstream resources where appropriate:

Base model: https://huggingface.co/Qwen/Qwen3-8B
Training data (public portion): https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus (ODC-By)

Citation

If you use this model in academic work, please cite:

bibtex

@misc{climategpt3,  title        = {ClimateGPT-3-8B},  howpublished = {\url{https://huggingface.co/Erasmus-AI/climategpt-3-8b}},  year         = {2026}}

Contact

If you have questions, issues, or evaluation results to share, please open a discussion/issue in the repository that accompanies this release.

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