inference: true
language:
- en
license: mit
model_creator: Mads Havmand
model_name: minillama
model_type: llama
quantized_by: Havmand
tags:
- llama
- test
- development
minillama
- Model creator: Mads Havmand
Description
minillama is a minimal Large Language Model using the Llama architecture and distributed in the GGUF format.
The purpose of the model is to be small and technically qualify as a model that can be loaded with llama.cpp without causing an error. I originally created this model because I needed a small model for my unit tests of Python code that used llama-cpp-python.
The model can technically be used for inference, but the output produced is a close to useless as you can get. Tokens per second is nice though, at around 1000 tokens per second on an Apple M2 Pro.
To reduce file size, the model is quantized using Q2_K.
The model contains 4.26 million parameters and is 3.26 MiB.
As for the vocabulary, the model uses the llama vocabulary provided by llama.cpp (SHA512: 38a5acf305050422882044df0acc97e5ae992ed19b2838b3b58ebbbb1f61c59bfc12a6f686a724aed32227045806e4dd46aadf9822155d1169455fa56d38fbc2)
The training corpus consists of a space and a newline:
00000000 20 0a | .|
00000002
Finally, the model was build using llama.cpp's train-text-from-scratch (from commit 97c1549808d2742d37584a3c9df28154bdf34417). The command used was:
./train-text-from-scratch \
--vocab-model models/ggml-vocab-llama.gguf \
--ctx 1 --embd 64 --head 1 --layer 1 \
--checkpoint-in chk-minillama-LATEST.gguf \
--checkpoint-out chk-minillama-ITERATION.gguf \
--model-out ggml-minillama-f32-ITERATION.gguf \
--train-data "training.txt" \
-t 6 -b 16 --seed 1 --adam-iter 1 \
--no-checkpointing
Quantization happened using ./quantize ggml-minillama-f32-LATEST.gguf 10.
These files were quantized using hardware kindly provided by me.