Run inference on the replit code instruct model using your CPU. This inference code uses a ggml quantized model. To run the model we'll use a library called ctransformers that has bindings to ggml in python.
Demo:
Using docker should make all of this easier for you. Minimum specs, system with 8GB of ram. Recommend to use python 3.10.
Will post some numbers for these two later.
First create a venv.
python -m venv env && source env/bin/activate
Next install dependencies.
pip install -r requirements.txt
Next download the quantized model weights (about 1.5GB).
python download_model.py
Ready to rock, run inference.
python inference.py
Next modify inference script prompt and generation parameters.
Python
100.0%
Run inference on the replit code instruct model using your CPU. This inference code uses a ggml quantized model. To run the model we'll use a library called ctransformers that has bindings to ggml in python.
Demo:
Using docker should make all of this easier for you. Minimum specs, system with 8GB of ram. Recommend to use python 3.10.
Will post some numbers for these two later.
First create a venv.
python -m venv env && source env/bin/activate
Next install dependencies.
pip install -r requirements.txt
Next download the quantized model weights (about 1.5GB).
python download_model.py
Ready to rock, run inference.
python inference.py
Next modify inference script prompt and generation parameters.
Python
100.0%