EVE-Instruct is a fine-tuned version of Mistral-Small-3.2-24B-Instruct-2506 specializing in Earth Intelligence, with particular emphasis on Earth Observation (EO) and Earth Science (ES) domains. It improves domain-specific capabilities while maintaining or exceeding the general capabilities of its base model.

EVE-Instruct-GGUF-Q3_K_M is a compressed (quantized) version designed for running on consumer hardware (CPU or low-VRAM GPUs). Compared to Q4_K_M, this variant offers a smaller footprint at the cost of slightly more accuracy loss — suitable when RAM is a hard constraint.

Quantization Details

Q3_K_M is a "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This results in a smaller file size than Q4_K_M with a moderate additional accuracy loss compared to the original bf16 weights. For accuracy benchmarks, refer to the base model card.

Usage

llama.cpp

bash

# Start a local OpenAI-compatible server:
llama-server -hf eve-esa/EVE-Instruct-GGUF-Q3_K_M:Q3_K_M

# Run inference directly in the terminal:
llama-cli -hf eve-esa/EVE-Instruct-GGUF-Q3_K_M:Q3_K_M

llama-cpp-python

python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="eve-esa/EVE-Instruct-GGUF-Q3_K_M",
    filename="EVE-Instruct-Q3_K_M.gguf",
)

llm.create_chat_completion(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful Earth Intelligence assistant specializing in Earth Observation and Earth Science."
        },
        {
            "role": "user",
            "content": "What is the Normalized Difference Vegetation Index (NDVI) and how is it used in remote sensing?"
        }
    ]
)

Ollama

ollama run hf.co/eve-esa/EVE-Instruct-GGUF-Q3_K_M:Q3_K_M

Note: This is a GGUF quantized file intended for local inference with llama.cpp-compatible runtimes (llama.cpp, Ollama, LM Studio, etc.). It is not compatible with vLLM, which requires the original safetensors weights from eve-esa/EVE-Instruct.

Funding

This project is supported by the European Space Agency (ESA) Φ-lab through the Large Language Model for Earth Observation and Earth Science project, as part of the Foresight Element within the FutureEO Block 4 programme.

Citation

If you use this model in academic or research settings, please cite the base model:

bibtex

@misc{eve-instruct-2025,
  title={EVE-Instruct: An Earth Intelligence Language Model},
  author={EVE-ESA},
  year={2025},
  note={arXiv:2508.09494},
  url={https://huggingface.co/eve-esa/EVE-Instruct}
}