Urban_CO2_Predictor_Small_LMM_LoRA_GGUF

Author: Dr. Sabri Allani AI & Cybersecurity Expert | R&D Consultant | Instructor | Open-Source Contributor Model type: GGUF (Urban_CO2_Predictor_Small_Edge_LLM based on Mistral-7B Instruct, quantized) Parameters: 7B File size: ~3GB License: MIT

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Overview

Overview

Urban CO2 Predictor Small LMM LoRA GGUF is a fine-tuned, quantized version of Mistral-7B Instruct designed for CO₂ emission prediction in urban mobility and VANET environments.
This work is part of:

• The international EIT Urban Mobility initiative (as part of the Urban Mobility HEB project): https://www.eiturbanmobility.eu/

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📥 Download model file:
➡️ Click here to download Urban_CO2_Predictor_Small_Edge_LLM.gguf
(~3.08 GB GGUF quantized model for edge inference and urban CO₂ prediction)

Intended Use

• Urban and environmental CO₂ emission analysis and prediction (baseline, demo, or experimental)
• LoRA and transfer learning for domain adaptation
• General language tasks on CPU/GPU (local, edge, or cloud inference)
• Research, prototyping, or educational work
• Prediction in VANETs and real-time urban mobility systems

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Example usage

from llama_cpp import Llama

# Load the quantized GGUF model
llm = Llama(model_path="co2_merged.gguf")

# Provide explicit parameters in the prompt
prompt = (
    "Estimate CO₂ emissions (g/km) for the following scenario:
"
    "- Location: Central Paris urban boulevard
"
    "- Timestamp: 07:45, weekday (rush hour)
"
    "- Weather: Cloudy, 18 °C
"
    "- Traffic density: 60 vehicles per km
"
    "- Vehicle mix: 70 % diesel Euro 6, 20 % petrol Euro 5, 10 % electric
"
    "- Average speed: 28 km/h
"
    "Return only the numeric estimate followed by 'g/km'."
)

response = llm(prompt)
print(response)

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Technical details

• Architecture: Mistral 7B, instruct-tuned
• File format: GGUF
• File size: ~3GB
• License: MIT

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Citation

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

@misc{allani2024urban,
  author       = {Dr. Sabri Allani},
  title        = {Urban CO2 Predictor Small LMM LoRA GGUF},
  year         = {2024},
  howpublished = {\url{https://huggingface.co/sallani/Urban_CO2_Predictor_Small_LMM_LoRA_GGUF}},
  note         = {In collaboration with EIT Urban Mobility}
}

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Contact

For questions or collaboration, contact me via:

• LinkedIn
• Hugging Face Profile

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Disclaimer

This model is provided as-is for research and development. I make no warranty for production use or accuracy in real-world CO₂ prediction tasks. Use at your own risk and adapt for your own projects as needed.

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License

MIT — Free to use, modify, and redistribute.

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Acknowledgment

This file is derived from Mistral-7B Instruct and adapted to GGUF format for open research in AI and environmental modeling.