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James Ravenscroft
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MODELS.md
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MODELS.md
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# Models Directory
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## "Coder" family models
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WizardCoder, StarCoder and SantaCoder are current "state-of-the-art" autocomplete models
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### SantaCoder (Best Small model)
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[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.
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This model is primarily trained on Python, Java and Javscript.
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| Model Name | RAM Requirement | Direct Download | HF Project Link |
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|---------------------|-----------------|-----------------|-----------------|
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| StarCoder | ~2GiB | [:arrow_down:](https://huggingface.co/mike-ravkine/gpt_bigcode-santacoder-GGML/resolve/main/santacoder-q4_0.bin) | [:hugs:](https://huggingface.co/mike-ravkine/gpt_bigcode-santacoder-GGML/) |
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To run in Turbopilot set model type `-m starcoder`
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### WizardCoder (Best Autocomplete Performance, Compute-Hungry)
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[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) ).
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Even when quantized, WizardCoder is a large model that takes up a significant amount of RAM.
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| Model Name | RAM Requirement | Direct Download | HF Project Link |
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|---------------------|-----------------|-----------------|-----------------|
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| WizardCoder | ~12GiB | [:arrow_down:](https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML/resolve/main/WizardCoder-15B-1.0.ggmlv3.q4_0.bin) | [:hugs:](https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML/) |
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To run in Turbopilot set model type `-m starcoder`
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### StarCoder
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[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.
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| Model Name | RAM Requirement | Direct Download | HF Project Link |
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|---------------------|-----------------|------------------|-----------------|
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| StarCoder | ~12GiB | [:arrow_down:](https://huggingface.co/NeoDim/starcoder-GGML/resolve/main/starcoder-ggml-q4_0.bin) | [:hugs:](https://huggingface.co/NeoDim/starcoder-GGML/) |
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| 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/) |
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To run in Turbopilot set model type `-m starcoder`
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## CodeGen 1.0
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The CodeGen models were the first models supported by Turbopilot. They perform less well than the newer Wizardcoder/Starcoder/Santacoder variant models.
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The `multi` flavour models can provide auto-complete suggestions for `C`, `C++`, `Go`, `Java`, `JavaScript`, and `Python`.
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The `mono` flavour models can provide auto-complete suggestions for `Python` only (but the quality of Python-specific suggestions may be higher).
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Pre-converted and pre-quantized models are available for download from here:
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| Model Name | RAM Requirement | Supported Languages | Direct Download | HF Project Link |
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|---------------------|-----------------|---------------------------|-----------------|-----------------|
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| CodeGen 350M multi | ~800MiB | `C`, `C++`, `Go`, `Java`, `JavaScript`, `Python` | [:arrow_down:](https://huggingface.co/ravenscroftj/CodeGen-350M-multi-ggml-quant/resolve/main/codegen-350M-multi-ggml-4bit-quant.bin) | [:hugs:](https://huggingface.co/ravenscroftj/CodeGen-350M-multi-ggml-quant) |
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| CodeGen 350M mono | ~800MiB | `Python` | [:arrow_down:](https://huggingface.co/Guglielmo/CodeGen-350M-mono-ggml-quant/resolve/main/ggml-model-quant.bin) | [:hugs:](https://huggingface.co/Guglielmo/CodeGen-350M-mono-ggml-quant) |
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| CodeGen 2B multi | ~4GiB | `C`, `C++`, `Go`, `Java`, `JavaScript`, `Python` | [:arrow_down:](https://huggingface.co/ravenscroftj/CodeGen-2B-multi-ggml-quant/resolve/main/codegen-2B-multi-ggml-4bit-quant.bin) | [:hugs:](https://huggingface.co/ravenscroftj/CodeGen-2B-multi-ggml-quant) |
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| CodeGen 2B mono | ~4GiB | `Python` | [:arrow_down:](https://huggingface.co/Guglielmo/CodeGen-2B-mono-ggml-quant/resolve/main/ggml-model-quant.bin) | [:hugs:](https://huggingface.co/Guglielmo/CodeGen-2B-mono-ggml-quant/) |
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| CodeGen 6B multi | ~8GiB | `C`, `C++`, `Go`, `Java`, `JavaScript`, `Python` | [:arrow_down:](https://huggingface.co/ravenscroftj/CodeGen-6B-multi-ggml-quant/resolve/main/codegen-6B-multi-ggml-4bit-quant.bin) | [:hugs:](https://huggingface.co/ravenscroftj/CodeGen-6B-multi-ggml-quant) |
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| CodeGen 6B mono | ~8GiB | `Python` | [:arrow_down:](https://huggingface.co/Guglielmo/CodeGen-6B-mono-ggml-quant/resolve/main/ggml-model-quant.bin) | [:hugs:](https://huggingface.co/Guglielmo/CodeGen-6B-mono-ggml-quant/) |
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14
README.md
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README.md
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@ -34,6 +34,12 @@ You have 2 options for getting the model
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You can download the pre-converted, pre-quantized models from Huggingface.
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For low RAM users (4-8 GiB), I recommend [SantaCoder](https://huggingface.co/mike-ravkine/gpt_bigcode-santacoder-GGML/resolve/main/santacoder-q4_0.bin) and for high power users (16+ GiB RAM, discrete GPU or apple silicon) I recomnmend [WizardCoder](https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML/resolve/main/WizardCoder-15B-1.0.ggmlv3.q4_0.bin).
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Turbopilot still supports the first generation codegen models from `v0.0.5` and earlier builds. Although old models do need to be requantized.
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You can find a full catalogue of models in [MODELS.md](MODELS.md).
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#### Option B: Convert The Models Yourself - Hard, More Flexible
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@ -57,7 +63,7 @@ If you have a multi-core system you can control how many CPUs are used with the
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./codegen-serve -t 6 -m starcoder -f ./models/santacoder-q4_0.bin
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```
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Turbopilot also supports the legacy codegen models. Just change the model type flag `-m` to `codegen` instead.
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To run the legacy codegen models. Just change the model type flag `-m` to `codegen` instead.
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**NOTE: the latest version of GGML requires that you re-quantize your codegen models. Old models downloaded from here will no longer work. I am working on providing updated quantized codegen models**
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@ -87,10 +93,12 @@ docker run --gpus=all --rm -it \
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-e THREADS=6 \
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-e MODEL="/models/codegen-2B-multi-ggml-4bit-quant.bin" \
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-p 18080:18080 \
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ghcr.io/ravenscroftj/turbopilot:v0.0.5-cuda
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ghcr.io/ravenscroftj/turbopilot:v1.0.0-cuda11
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```
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You will need CUDA 11 or later to run this container. You should be able to see `/app/codegen-serve` listed when you run `nvidia-smi`.
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Swap `ghcr.io/ravenscroftj/turbopilot:v1.0.0-cuda11` for `ghcr.io/ravenscroftj/turbopilot:v1.0.0-cuda12` if you are using CUDA 12 or later.
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You will need CUDA 11 or CUDA 12 later to run this container. You should be able to see `/app/turbopilot` listed when you run `nvidia-smi`.
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#### Executable and CUDA
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