CodeGen
server
CodeGen | server | |
---|---|---|
18 | 24 | |
4,769 | 7,356 | |
0.9% | 2.7% | |
6.1 | 9.5 | |
about 2 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CodeGen
- FLaNK Stack Weekly 23 Oct 2023
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Seeking Resources on Open-source Large Language Models Specializing in Causality Tasks
CodeGen (I included this model as there's some evidence in recent months that LLMs trained on code-related tasks better capture long-range dependencies compared to LLMs trained on 'just words')
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Free alternative to OpenAI Copilot
https://github.com/salesforce/CodeGen these are used in fauxpilot https://github.com/fauxpilot/fauxpilot
- FauxPilot – an open-source GitHub Copilot server
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GitHub Copilot X: The AI-powered developer experience
You can always use https://github.com/salesforce/CodeGen . But it does require managing the model hosting. You can use fauxpilot to mimic copilot functionality https://github.com/fauxpilot/fauxpilot
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Will we see a “stable diffusion” version of ChatGPT?
freediver 2 days ago | prev | next [–] Here is an example of one general purpose open source LLM, probably the best you can get: https://github.com/EleutherAI/gpt-neox To manage your expectations it is nowhere as good as ChatGPT. If you are interested in programming only: https://github.com/salesforce/CodeGen
- Codegen: Open-Source Codex Equivalent
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Exploring Ghostwriter, a GitHub Copilot alternative
Replit built Ghostwriter on the open source scene based on Salesforce’s Codegen, using Nvidia’s FasterTransformer and Triton server for highly optimized decoders, and the knowledge distillation process of the CodeGen model from two billion parameters to a faster model of one billion parameters.
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We've filed a lawsuit against GitHub Copilot
https://github.com/moyix/fauxpilot is working on an open source variant. Based on https://github.com/salesforce/CodeGen
- Open Source model to generate code competitive with OpenAI Codex/GitHub Copilot
server
- FLaNK Weekly 08 Jan 2024
- Is there any open source app to load a model and expose API like OpenAI?
- "A matching Triton is not available"
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best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?
- Triton Inference Server - Backend
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Single RTX 3080 or two RTX 3060s for deep learning inference?
For inference of CNNs, memory should really not be an issue. If it is a software engineering problem, not a hardware issue. FP16 or Int8 for weights is fine and weight size won’t increase due to the high resolution. And during inference memory used for hidden layer tensors can be reused as soon as the last consumer layer has been processed. You likely using something that is designed for training for inference and that blows up the memory requirement, or if you are using TensorRT or something like that, you need to be careful to avoid that every tasks loads their own copy of the library code into the GPU. Maybe look at https://github.com/triton-inference-server/server
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Machine Learning Inference Server in Rust?
I am looking for something like [Triton Inference Server](https://github.com/triton-inference-server/server) or [TFX Serving](https://www.tensorflow.org/tfx/guide/serving), but in Rust. I came across [Orkon](https://github.com/vertexclique/orkhon) which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](https://github.com/vaaaaanquish/Awesome-Rust-MachineLearning)
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Multi-model serving options
You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
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I mean,.. we COULD just make our own lol
[1] https://docs.nvidia.com/launchpad/ai/chatbot/latest/chatbot-triton-overview.html[2] https://github.com/triton-inference-server/server[3] https://neptune.ai/blog/deploying-ml-models-on-gpu-with-kyle-morris[4] https://thechief.io/c/editorial/comparison-cloud-gpu-providers/[5] https://geekflare.com/best-cloud-gpu-platforms/
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Why TensorFlow for Python is dying a slow death
"TensorFlow has the better deployment infrastructure"
Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).
For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.
Triton Server is very impressive and I'm always surprised to see how relatively niche it is.
[0] - https://github.com/triton-inference-server/server
What are some alternatives?
fauxpilot - FauxPilot - an open-source alternative to GitHub Copilot server
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
pifs - πfs - the data-free filesystem!
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
FasterTransformer - Transformer related optimization, including BERT, GPT
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
copilot
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
Triton - Triton is a dynamic binary analysis library. Build your own program analysis tools, automate your reverse engineering, perform software verification or just emulate code.
cformers - SoTA Transformers with C-backend for fast inference on your CPU.
Megatron-LM - Ongoing research training transformer models at scale