## StableCode Instruct State-of-the-art for low Spec machines(Released 8th August 2023)
[StableCode](https://stability.ai/blog/stablecode-llm-generative-ai-coding) Instruct is a new model from [Stability.ai](https://stability.ai/) which provides reasonable autocomplete suggestions in approx 3GiB of RAM.
| Model Name | RAM Requirement | Direct Download | HF Project Link |
[SantaCoder](https://huggingface.co/bigcode/santacoder) is a smaller version of the StarCoder and WizardCoder family with only 1.1 Billion parameters. The model is trained with fill-in-the-middle objective allowing it to be used to auto-complete function parameters.
This model is primarily trained on Python, Java and Javscript.
| Model Name | RAM Requirement | Direct Download | HF Project Link |
[WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder) is the current SOTA auto complete model, it is an updated version of StarCoder that achieves 57.1 pass@1 on HumanEval benchmarks (essentially in 57% of cases it correctly solves a given challenge. Read more about how this metric works in the scientific paper [here](https://arxiv.org/pdf/2107.03374.pdf) ).
Even when quantized, WizardCoder is a large model that takes up a significant amount of RAM.
| Model Name | RAM Requirement | Direct Download | HF Project Link |
[StarCoder](https://huggingface.co/blog/starcoder) held the previous title of state-of-the-art coding model back in May 2023. It is still a reasonably good model by comparison but it is a similar size and has similar RAM and compute requirements to WizardCoder so you may be better off just running that. Links below provided for posterity.
| Model Name | RAM Requirement | Direct Download | HF Project Link |
| StarCoder Plus | ~12GiB | [:arrow_down:](https://huggingface.co/TheBloke/starcoderplus-GGML/resolve/main/starcoderplus.ggmlv3.q4_0.bin) | [:hugs:](https://huggingface.co/TheBloke/starcoderplus-GGML/) |
To run in Turbopilot set model type `-m starcoder`
## CodeGen 1.0
The CodeGen models were the first models supported by Turbopilot. They perform less well than the newer Wizardcoder/Starcoder/Santacoder variant models.
The `multi` flavour models can provide auto-complete suggestions for `C`, `C++`, `Go`, `Java`, `JavaScript`, and `Python`.
The `mono` flavour models can provide auto-complete suggestions for `Python` only (but the quality of Python-specific suggestions may be higher).
Pre-converted and pre-quantized models are available for download from here:
| Model Name | RAM Requirement | Supported Languages | Direct Download | HF Project Link |
