Optimum is an extension of Transformers that provides a set of performance optimization tools to train and run models on targeted hardware with maximum efficiency. It is also the repository of small, mini, tiny models.

BERT model home on github

Repository of all Bert models, including small. Start using this model for testing.

Comparison of efficiency of all LLM models on hugging face

GGML quantized models. They would let you leverage CPU and system RAM, instead of having to rely on a GPU’s. This could save you a fortune, especially if go for some used AMD Epyc platforms. This could be more viable for the larger models, especially the 30B/65B parameters models which would still press or exceed the VRAM on the P40.

High level how to train a model

Research community developing various code models, small and big. Models may not be instruct

Another open source small (1B) model.

They have the 1.3B version!!! This may be the best to start with Newspeak. Should work train even on huggingcface

Another possible model. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.

With the optimizers of bitsandbytes (like 8 bit AdamW), you would need 2 bytes per parameter, or 14 GB of GPU memory.

Another potential model to use for Newspeak, but it is NOT open source. Adventage: 2.5B params, so should be usable in small GPUs

Comparison of LLM models for coding

Open source with lots of information. Uses Multiple undrelying models. Not sure how I would train for it

The Mixtral model is new, and seems to be good. Click on “Demo“ to test it

Article has comparison with other code-LLM models

Includes code generation models

List of LLM models on Wikipedia

Hi level only talk about training for a language

Describes how to train a new language (desperanto) model.