cog | llama.cpp | |
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20 | 780 | |
7,186 | 57,984 | |
3.2% | - | |
9.4 | 10.0 | |
6 days ago | 5 days ago | |
Python | C++ | |
Apache License 2.0 | MIT 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.
cog
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AI Grant Traction in OSS Startups
View on GitHub
- Insanely Fast Whisper: Transcribe 300 minutes of audio in less than 98 seconds
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Talk-Llama
I'm in the same situation. I found this cog project to dockerise ML https://github.com/replicate/cog : you write just one python class and a yaml file, and it takes care of the "CUDA hell" and deps. It even creates a flask app in front of your model.
That helps keep your system clean, but someone with big $s please rewrite pytorch to golang or rust or even nodejs / typescript.
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Llama 2 – Meta AI
https://github.com/replicate/cog
Our thinking was just that a bunch of folks will want to fine-tune right away, then deploy the fine-tunes, so trying to make that easy... Or even just deploy the models-as-is on their own infra without dealing with CUDA insanity!
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Handling concurrent requests to ML model API
I have used this tool before: https://github.com/replicate/cog/tree/main
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Opinions on Cog: Containers for machine learning
Then I discovered Cog: Containers for Machine Learning. Looks like a way more flexible solution to plug in the existing infrastructure: you write your custom code and Cog plugs it in a Docker image with FastAPI, no extra ecosystem complexity added.
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can someone teach me how to install the new stable diffusion repo?
Highly recommend using cog https://github.com/replicate/cog
- Run Stable Diffusion on Your M1 Mac’s GPU
- replicate/cog: Containers for machine learning
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Why companies move off Heroku (besides the cost)
Dokku Maintainer here.
Dokku also supports Dockerfiles, Docker Images, Tarballs (similar to heroku slugs), and Cloud Native Buildpacks. I'm also actively working on AWS Lambda support (both for simple usage without much config as well as SAM-based usage) and investigating Replicate's Cog[1] and Railways Nixpacks[2] functionalities for building apps.
There are quite a few options in the OSS space (as well as Commercial offerings from new startups and popular incumbents). It's an interesting space to be in, and its always fun to see how new offerings innovate on existing solutions.
[1] https://github.com/replicate/cog
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
What are some alternatives?
nixpacks - App source + Nix packages + Docker = Image
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
pytorch_wavelets - Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
gpt4all - gpt4all: run open-source LLMs anywhere
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
heroku-review-app-actions - GitHub action to automate managing review apps on your Heroku account
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
ggml - Tensor library for machine learning
memray - Memray is a memory profiler for Python
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM