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https://github.com/ravenscroftj/turbopilot.git
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sort out args
This commit is contained in:
James Ravenscroft
parent
f5c0bbcb3f
commit
430733c7b8
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@ -1,31 +0,0 @@
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FROM nvidia/cuda:11.7.1-devel-ubuntu22.04 AS build
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RUN apt-get update && apt-get install -y build-essential cmake libboost-dev libasio-dev
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ADD ./ /turbopilot
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RUN mkdir /turbopilot/build
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WORKDIR /turbopilot/build
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RUN cmake -DGGML_CUBLAS=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc ..
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RUN make turbopilot
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FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu22.04 AS runtime
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WORKDIR /app
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COPY --from=build /turbopilot/build/bin/turbopilot /app/turbopilot
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ENV THREADS=4
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ENV MODEL="/models/codegen-2B-multi-ggml-4bit-quant.bin"
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ENV BATCHSIZE=64
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COPY ./run.sh /app/
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EXPOSE 18080
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CMD /app/run.sh
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33
README.md
33
README.md
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@ -10,7 +10,9 @@ TurboPilot is a self-hosted [copilot](https://github.com/features/copilot) clone
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**NEW:** As of v0.0.5 turbopilot supports cuda inference which greatly accelerates suggestions when working with longer prompts (i.e. longer existing code files).
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**New: Refactored + Simplified**: The source code has been improved to make it easier to extend and add new models to Turbopilot. The system now supports multiple flavours of model
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**New: Wizardcoder, Starcoder, Santacoder support** - Turbopilot now supports state of the art local code completion models which provide more programming languages and "fill in the middle" support.
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## 🤝 Contributing
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@ -23,6 +25,7 @@ Make a fork, make your changes and then open a [PR](https://github.com/ravenscro
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The easiest way to try the project out is to grab the pre-processed models and then run the server in docker.
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### Getting The Models
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You have 2 options for getting the model
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@ -31,21 +34,6 @@ 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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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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#### Option B: Convert The Models Yourself - Hard, More Flexible
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@ -58,17 +46,21 @@ Download the [latest binary](https://github.com/ravenscroftj/turbopilot/releases
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Run:
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```bash
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./codegen-serve -m ./models/codegen-6B-multi-ggml-4bit-quant.bin
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./turbopilot -m starcoder -f ./models/santacoder-q4_0.bin
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```
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The application should start a server on port `18080`
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The application should start a server on port `18080`, you can change this with the `-p` option but this is the default port that vscode-fauxpilot tries to connect to so you probably want to leave this alone unless you are sure you know what you're doing.
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If you have a multi-core system you can control how many CPUs are used with the `-t` option - for example, on my AMD Ryzen 5000 which has 6 cores/12 threads I use:
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```bash
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./codegen-serve -t 6 -m ./models/codegen-6B-multi-ggml-4bit-quant.bin
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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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**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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### 📦 Running From Docker
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You can also run Turbopilot from the pre-built docker image supplied [here](https://github.com/users/ravenscroftj/packages/container/package/turbopilot)
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@ -79,7 +71,8 @@ You will still need to download the models separately, then you can run:
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docker run --rm -it \
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-v ./models:/models \
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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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-e MODEL_TYPE=starcoder \
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-e MODEL="/models/santacoder-q4_0.bin" \
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-p 18080:18080 \
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ghcr.io/ravenscroftj/turbopilot:latest
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```
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2
run.sh
2
run.sh
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@ -1,3 +1,3 @@
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#!/bin/sh
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/app/codegen-serve -t $THREADS -m $MODEL -b $BATCHSIZE
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/app/turbopilot -t $THREADS -m $MODEL_TYPE -f $MODEL
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12
src/main.cpp
12
src/main.cpp
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@ -22,10 +22,16 @@ int main(int argc, char **argv)
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.help("Path to the model that turbopilot should serve")
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.required();
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program.add_argument("-t", "--model-type")
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.help("The type of model to load. Can be codegen/gpt-j or starcoder architectures.")
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program.add_argument("-m", "--model-type")
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.help("The type of model to load. Can be codegen,starcoder,wizardcoder")
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.default_value("codegen");
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program.add_argument("-t", "--threads")
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.help("The number of CPU threads turbopilot is allowed to use. Defaults to 4")
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.default_value(4)
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.scan<'i', int>();
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program.add_argument("-p", "--port")
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.help("The tcp port that turbopilot should listen on")
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.default_value(18080)
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@ -62,6 +68,8 @@ int main(int argc, char **argv)
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ModelConfig config{};
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std::mt19937 rng(program.get<int>("--random-seed"));
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config.n_threads = program.get<int>("--threads");
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if(model_type.compare("codegen") == 0) {
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spdlog::info("Initializing GPT-J type model for '{}' model", model_type);
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model = new GPTJModel(config, rng);
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