pixilated730/Replit_LLM

Run LLM on your CPU machine

0

stars

2

commits

Python

primary language

Jun 29, 2023

updated

README

Replit Code Instruct inference using CPU

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:

Inference Demo

Requirements

Using docker should make all of this easier for you. Minimum specs, system with 8GB of ram. Recommend to use python 3.10.

Tested working on

Will post some numbers for these two later.

  • AMD Epyc 7003 series CPU
  • AMD Ryzen 5950x CPU
  • Mac M1

Setup

First create a venv.

python -m venv env && source env/bin/activate

Next install the submodule with ctransformers patch.

git submodule update --init --recursive

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.

Contributors

pixilated730

2 commits

pixilated730/Replit_LLM

Run LLM on your CPU machine

0

stars

2

commits

Python

primary language

Jun 29, 2023

updated

README

Replit Code Instruct inference using CPU

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:

Inference Demo

Requirements

Using docker should make all of this easier for you. Minimum specs, system with 8GB of ram. Recommend to use python 3.10.

Tested working on

Will post some numbers for these two later.

  • AMD Epyc 7003 series CPU
  • AMD Ryzen 5950x CPU
  • Mac M1

Setup

First create a venv.

python -m venv env && source env/bin/activate

Next install the submodule with ctransformers patch.

git submodule update --init --recursive

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.

Contributors

pixilated730

2 commits

Languages

Python

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