DeepSeek-Coder
metaflow
DeepSeek-Coder | metaflow | |
---|---|---|
8 | 24 | |
5,567 | 7,644 | |
8.9% | 2.0% | |
8.6 | 9.2 | |
about 1 month ago | 5 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
DeepSeek-Coder
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Meta Llama 3
deepseek-coder-instruct 6.7B still looks like is better than llama 3 8B on HumanEval [0], and deepseek-coder-instruct 33B still within reach to run on 32 GB Macbook M2 Max - Lamma 3 70B on the other hand will be hard to run locally unless you really have 128GB ram or more. But we will see in the following days how it performs in real life.
[0] https://github.com/deepseek-ai/deepseek-coder?tab=readme-ov-...
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Mistral Remove "Committing to open models" from their website
Deepseek (https://github.com/deepseek-ai/DeepSeek-Coder?tab=readme-ov-...) code is MIT and the model license is available too.
- FLaNK Stack 05 Feb 2024
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Stable Code 3B: Coding on the Edge
https://github.com/deepseek-ai/deepseek-coder
33B Instruct doesn’t beat 6.7B Instruct by much but maybe those % improvements mean more for your usage.
I run 6.7B since I have 16GB RAM.
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What the heck is so great about this model?
Deepseek Coder: https://github.com/deepseek-ai/DeepSeek-Coder (Best open source coding model right now)
- Deepseek Coder instruct – 6.7B model beats gpt3.5-turbo in coding
- FLaNK Stack Weekly for 13 November 2023
- DeepSeek-Coder: Has anyone tried this one?
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
draw-a-ui - Draw a mockup and generate html for it
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
FT-Merge-Quantize-Infer-CML
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
cucim - cuCIM - RAPIDS GPU-accelerated image processing library
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
linen.dev - Lightweight Google-searchable Slack alternative for Communities
kedro-great - The easiest way to integrate Kedro and Great Expectations
wubloader
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
clipea - 📎🟢 Like Clippy but for the CLI. A blazing fast AI helper for your command line
dvc - 🦉 ML Experiments and Data Management with Git