What it knows

The model has expert knowledge across all 6 EU framework programmes:

It can answer questions about:

• 🔬 Project details: objectives, methodology, expected impacts
• 💰 Funding information: total costs, EC contributions, funding schemes
• 📅 Timelines: start/end dates, project duration
• 🏢 Organizations: coordinators, participants, country information
• 🧬 Scientific domains: EuroSciVoc classifications, research topics
• 📊 Programme-level statistics: funding distribution, topic clusters
• 🔄 Cross-programme comparisons: funding trends across FP4→Horizon Europe

Usage

With PEFT (recommended)

python
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from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, "RCaz/Qwen2.5-7B-EU-Funding-Expert")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

messages = [
    {"role": "system", "content": "You are an expert assistant specializing in European Union research funding programmes."},
    {"role": "user", "content": "What were the main funding priorities under Horizon 2020?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Example prompts

• "Tell me about the ITER project and its EU funding."
• "How much did the EU invest in quantum computing research under Horizon 2020?"
• "Compare the total budgets of FP7 and Horizon 2020."
• "Which organizations coordinated the most EU-funded AI projects?"
• "What scientific domains received the highest funding in Horizon Europe?"

Training details

Dataset

The training data was generated from official CORDIS CSV exports covering 126K+ EU-funded research projects. Eight types of Q&A conversations were created:

1. Project overview — What is the project about?
2. Funding details — How much funding did it receive?
3. Timeline — When did it start/end?
4. Organizations — Who coordinates/participates?
5. Scientific domains — What fields does it cover?
6. Topics — What EU topics/calls is it associated with?
7. Programme-level — Statistics and trends within a programme
8. Cross-programme — Comparisons across framework programmes

All conversations follow the ChatML format with a system prompt establishing EU funding expertise.

Limitations

• Knowledge is based on CORDIS data snapshots and may not reflect the very latest project updates
• The model is specialized for EU funding — it may be less capable on general knowledge tasks compared to the base model
• Financial figures and project details are as accurate as the source CORDIS data
• The model may occasionally hallucinate details for very specific project queries

Citation

If you use this model, please cite:

bibtex
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@misc{rcaz2026eufunding,
  title={Qwen2.5-7B-EU-Funding-Expert: A Fine-tuned LLM for European Research Funding},
  author={RCaz},
  year={2026},
  url={https://huggingface.co/RCaz/Qwen2.5-7B-EU-Funding-Expert}
}

Acknowledgments

• Training data sourced from CORDIS — © European Union
• Base model by Qwen Team
• Fine-tuned using TRL and PEFT