Optional: Converting a Model to GGUF and Quantizing

The latest llama.cpp framework requires the model to be converted into GGUF format. GGUF is a quantization technique. Quantization is a technique used to reduce the size of large neural networks, including large language models (LLMs) by modifying the precision of their weights. If you have a model already in GGUF format, you can skip this step.

Clone the llama.cpp repository

git clone https://github.com/ggerganov/llama.cpp.git

Set up the virtual environment

cd llama.cpp
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Modify the conversion script

The conversion script has a bug when converting the InstructLab 🥼 model.

In convert-hf-to-gguf.py, add the following lines (with +):

def write_tensors(self):
    self.gguf_writer.add_tensor(new_name, data)
+   if new_name == "token_embd.weight":
+       self.gguf_writer.add_tensor("output.weight", data)
def write(self):

Convert a model to GGUF

The following command converts a Hugging Face model (safetensors) to GGUF format and saves it in your model directory with a .gguf extension.

export MODEL_DIR={model_directory}
python convert-hf-to-gguf.py $MODEL_DIR --outtype f16

Note: This may take about a minute or so.


Optionally, for smaller/faster models with varying loss of quality use a quantized model.

Make the llama.cpp binaries

Build binaries like quantize etc. for your environment.


Run quantize command

./quantize {model_directory}/{f16_gguf_model} <type>

For example, the following command converts the f16 GGUF model to a Q4_K_M quantized model and saves it in your model directory with a <type>.gguf suffix (e.g. ggml-model-Q4_K_M.gguf).

./quantize $MODEL_DIR/ggml-model-f16.gguf Q4_K_M

Tip: Use ./quantize help for a list of quantization types with their relative size and output quality along with additional usage parameters.