Instructions to use Erasmus-AI/climategpt-3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Erasmus-AI/climategpt-3-8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Erasmus-AI/climategpt-3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Erasmus-AI/climategpt-3-8b")
model = AutoModelForCausalLM.from_pretrained("Erasmus-AI/climategpt-3-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Erasmus-AI/climategpt-3-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Erasmus-AI/climategpt-3-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Erasmus-AI/climategpt-3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Erasmus-AI/climategpt-3-8b

SGLang

How to use Erasmus-AI/climategpt-3-8b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Erasmus-AI/climategpt-3-8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Erasmus-AI/climategpt-3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Erasmus-AI/climategpt-3-8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Erasmus-AI/climategpt-3-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Erasmus-AI/climategpt-3-8b with Docker Model Runner:
```
docker model run hf.co/Erasmus-AI/climategpt-3-8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ClimateGPT-3-8B

ClimateGPT-3-8B is an open language model domain-adapted for climate science and the Planetary Boundaries framework.

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:

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
`planetary_boundaries_mcq_hard`	acc_norm	0.3800	0.3400
`planetary_boundaries_qa_large`	exact_match	0.9000	0.8467
`planetary_boundaries_qa_strict_core_nolist`	exact_match	0.6556	0.4889

How to use

Transformers

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

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:

@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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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Erasmus-AI/climategpt-3-8b

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1590)

this model

Quantizations

1 model

Erasmus-AI
/

climategpt-3-8b

ClimateGPT-3-8B

Model details

Intended use

Limitations

Training

Continued pretraining (CPT)

Supervised fine-tuning (SFT)

Training data

Public data

Non-public / generated data

Evaluation

How to use

Transformers

vLLM

License

Attribution

Citation

Contact

Model tree for Erasmus-AI/climategpt-3-8b

Dataset used to train Erasmus-AI/climategpt-3-8b