tabby
autodistill
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tabby | autodistill | |
---|---|---|
24 | 13 | |
17,192 | 1,529 | |
6.2% | 9.5% | |
9.9 | 9.2 | |
5 days ago | 24 days ago | |
Rust | Python | |
GNU General Public License v3.0 or later | 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.
tabby
- Google CodeGemma: Open Code Models Based on Gemma [pdf]
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What AI assistants are already bundled for Linux?
NixOS just got tabbyml[1] which is built on llama-cpp. Working on systemsd services the weekend and updating latest tabbyml release which supports rocm in addition to cuda
[1] https://github.com/TabbyML/tabby
[2] https://github.com/NixOS/nixpkgs/pull/291744
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: Tabby back end in 20 Python lines (self-hosted AI coding assistant)
Nice implementation! It should serve as a great reference for a minimal Tabby's backend API. Thank you for sharing it!
Yeah - ultimately, it won't be as performant or feature-rich compared to https://github.com/TabbyML/tabby, but it's still perfect for educational purposes!
- Stable Code 3B: Coding on the Edge
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Show HN: I built local copilot alternative using Codellama
Looks interesting! What are the main differences between this and https://github.com/TabbyML/tabby ?
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Ask HN: Who is hiring? (October 2023)
TabbyML | Software Engineer (Rust) | REMOTE
Self-hosted AI coding assistant. An opensource / on-prem alternative to GitHub Copilot.
Project: https://github.com/TabbyML/tabby
Tabby is seeking a Software Engineer proficient in Rust to join our core engineering team. In this role, you will be responsible for developing the following features:
- Show HN: Tabby – AI Coding Assistant Runs on Apple M1/M2 GPU
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Meta: Code Llama, an AI Tool for Coding
There are a bunch of VSCode extensions that make use of local models. Tabby seems to be the most friendly right now, but I admittedly haven't tried it myself: https://tabbyml.github.io/tabby/
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CodeCompose: Meta’s AI Coding Assistant
Check out https://github.com/TabbyML/tabby, which is fully self-hostable and comes with niche features. On M1/M2, it offers a convenient single binary deployment, thanks to Rust. You can find the latest release at https://github.com/TabbyML/tabby/releases/tag/latest.
(Disclaimer: I am the author)
autodistill
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Ask HN: Who is hiring? (February 2024)
Roboflow | Open Source Software Engineer, Web Designer / Developer, and more. | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0224
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].
We have several openings available but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping us figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)
We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.
[1]: https://roboflow.com/?ref=whoishiring0224
[2]: https://roboflow.com/universe?ref=whoishiring0224
[3]: https://github.com/autodistill/autodistill
[4]: https://github.com/roboflow/supervision
[5]: https://blog.roboflow.com/?ref=whoishiring0224
[6]: https://www.youtube.com/@Roboflow
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Is supervised learning dead for computer vision?
The places in which a vision model is deployed are different than that of a language model.
A vision model may be deployed on cameras without an internet connection, with data retrieved later; a vision model may be used on camera streams in a factory; sports broadcasts on which you need low latency. In many cases, real-time -- or close to real-time -- performance is needed.
Fine-tuned models can deliver the requisite performance for vision tasks with relatively low computational power compared to the LLM equivalent. The weights are small relative to LLM weights.
LLMs are often deployed via API. This is practical for some vision applications (i.e. bulk processing), but for many use cases not being able to run on the edge is a dealbreaker.
Foundation models certainly have a place.
CLIP, for example, works fast, and may be used for a task like classification on videos. Where I see opportunity right now is in using foundation models to train fine-tuned models. The foundation model acts as an automatic labeling tool, then you can use that model to get your dataset. (Disclosure: I co-maintain a Python package that lets you do this, Autodistill -- https://github.com/autodistill/autodistill).
SAM (segmentation), CLIP (embeddings, classification), Grounding DINO (zero-shot object detection) in particular have a myriad of use cases, one of which is automated labeling.
I'm looking forward to seeing foundation models improve for all the opportunities that will bring!
- Ask HN: Who is hiring? (October 2023)
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Autodistill: A new way to create CV models
Autodistill
- Show HN: Autodistill, automated image labeling with foundation vision models
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Show HN: Pip install inference, open source computer vision deployment
Thanks for the suggestion! Definitely agree, we’ve seen that work extremely well for Supervision[1] and Autodistill, some of our other open source projects.
There’s still a lot of polish like this we need to do; we’ve spent most of our effort cleaning up the code and documentation to prep for open sourcing the repo.
Next step is improving the usability of the pip pathway (that interface was just added; the http server was all we had for internal use). Then we’re going to focus on improving the content and expanding the models it supports.
[1] https://github.com/roboflow/supervision
[2] https://github.com/autodistill/autodistill
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Ask HN: Who is hiring? (August 2023)
Roboflow | Multiple Roles | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0823
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].
We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)
We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.
[1]: https://roboflow.com/?ref=whoishiring0823
[2]: https://roboflow.com/universe?ref=whoishiring0823
[3]: https://github.com/autodistill/autodistill
[4]: https://github.com/roboflow/supervision
[5]: https://blog.roboflow.com/?ref=whoishiring0823
[6]: https://www.youtube.com/@Roboflow
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AI That Teaches Other AI
> Their SKILL tool involves a set of algorithms that make the process go much faster, they said, because the agents learn at the same time in parallel. Their research showed if 102 agents each learn one task and then share, the amount of time needed is reduced by a factor of 101.5 after accounting for the necessary communications and knowledge consolidation among agents.
This is a really interesting idea. It's like the reverse of knowledge distillation (which I've been thinking about a lot[1]) where you have one giant model that knows a lot about a lot & you use that model to train smaller, faster models that know a lot about a little.
Instead, you if you could train a lot of models that know a lot about a little (which is a lot less computationally intensive because the problem space is so confined) and combine them into a generalized model, that'd be hugely beneficial.
Unfortunately, after a bit of digging into the paper & Github repo[2], this doesn't seem to be what's happening at all.
> The code will learn 102 small and separte heads(either a linear head or a linear head with a task bias) for each tasks respectively in order. This step can be parallized on multiple GPUS with one task per GPU. The heads will be saved in the weight folder. After that, the code will learn a task mapper(Either using GMMC or Mahalanobis) to distinguish image task-wisely. Then, all images will be evaluated in the same time without a task label.
So the knowledge isn't being combined (and the agents aren't learning from each other) into a generalized model. They're just training a bunch of independent models for specific tasks & adding a model-selection step that maps an image to the most relevant "expert". My guess is you could do the same thing using CLIP vectors as the routing method to supervised models trained on specific datasets (we found that datasets largely live in distinct regions of CLIP-space[3]).
[1] https://github.com/autodistill/autodistill
[2] https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learnin...
[3] https://www.rf100.org
- Autodistill: Use foundation vision models to train smaller, supervised models
- Autodistill: use big slow foundation models to train small fast supervised models (r/MachineLearning)
What are some alternatives?
fauxpilot - FauxPilot - an open-source alternative to GitHub Copilot server
anylabeling - Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
turbopilot - Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU
Shared-Knowledge-Lifelong-Learnin
refact - WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding
segment-geospatial - A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
opentofu - OpenTofu lets you declaratively manage your cloud infrastructure.
aider - aider is AI pair programming in your terminal
supervision - We write your reusable computer vision tools. 💜
ollama-ui - Simple HTML UI for Ollama
flockfysh - A simple data vending machine that pops more out that what comes in. Use flockfysh to seamlessly pool existing datasets with quality web-scraped data to get top notch datasets.