Promising stuff from their repo, claiming "exceptional performance, achieving a [HumanEval] pass@1 score of 57.3, surpassing the open-source SOTA by approximately 20 points."
It is StarCoder and fine tuned on a new Wizard Instruct dataset optimized for coding models. So it follows the instruct formatting of prompts on top of the StarCoder base model.
It's a different type of model. llama.cpp only supports LLaMA models while GGML (the machine learning library llama.cpp is based on) has examples of various models with different architectures. WizardCoder, MPT, Bloom, probably very soon Falcon. Also some separate projects use GGML to support other models (including some of the ones I listed). For example the Rust "llm" project can support LLaMA models, MPT, BLOOM.
It looks like a frontend that just bundles a bunch of stuff together. Oobabooga's webui thing is similar: you can run stuff with llama.cpp, GPTQ, etc. What models and features are supported is going to depend on how the frontend manages that stuff. There are also forks of llama.cpp like koboldc++ which may support different models/features/formats (I know koboldc++ supports some older GGML file formats that llama.cpp broke compatibility with).
I don't know if it does or doesn't, I was just saying those two projects seemed similar: presenting a frontend for running inference on models while the user doesn't necessarily have to know/care what backend is used.
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