RuntimeError
llama_cpp.RuntimeError: GGUF model load error incompatible
Stack trace
Traceback (most recent call last):
File "app.py", line 42, in <module>
model = LLM(model_path="model.gguf") # triggers error
File "llama_cpp/llama.py", line 123, in __init__
raise RuntimeError("GGUF model load error incompatible")
RuntimeError: GGUF model load error incompatible Why it happens
This error happens because the GGUF model file uses a quantization format or version not supported by the installed llama.cpp library. The model may be built with a newer or different quantization scheme than the llama.cpp runtime expects.
Detection
Check the llama.cpp library version and the GGUF model quantization format before loading. Validate compatibility by verifying model metadata or using llama.cpp tools to inspect the model file.
Causes & fixes
The GGUF model was quantized with a newer or unsupported quantization format.
Re-quantize the model using a supported quantization format compatible with your llama.cpp version or update llama.cpp to the latest version supporting the model's format.
Mismatch between the llama.cpp library version and the GGUF model file version.
Upgrade your llama.cpp Python bindings and native library to the latest release that supports the GGUF model version you are using.
Corrupted or partially downloaded GGUF model file causing format read errors.
Verify the integrity of the GGUF model file by re-downloading or re-exporting the model from the source.
Code: broken vs fixed
from llama_cpp import LLM
model = LLM(model_path="model.gguf") # triggers RuntimeError: GGUF model load error incompatibleimport os
from llama_cpp import LLM
# Ensure environment variable for llama.cpp path or config if needed
os.environ["LLAMA_CPP_MODEL_PATH"] = "model.gguf"
# Use updated llama.cpp version supporting the model's quantization format
model = LLM(model_path=os.environ["LLAMA_CPP_MODEL_PATH"])
print("Model loaded successfully")Workaround
If you cannot update llama.cpp immediately, convert the GGUF model back to a supported older quantization format using compatible tools or fallback to a non-quantized model version temporarily.
Prevention
Always verify the quantization format compatibility between your GGUF model and llama.cpp version before deployment. Automate model validation and keep llama.cpp updated to support new quantization schemes.