guardrails
segment-anything
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guardrails | segment-anything | |
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13 | 56 | |
3,284 | 44,026 | |
9.8% | 3.2% | |
9.9 | 4.2 | |
5 days ago | 10 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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guardrails
- Guardrails AI
- Does anyone have an example of a langchain based customer facing agent like a cashier/waitress?
- Is there a UI that can limit LLM tokens to a preset list?
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A minimal design pattern for LLM-powered microservices with FastAPI & LangChain
You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails
- Ask HN: People who were laid off or quit recently, how are you doing?
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Ask HN: AI to study my DSL and then output it?
There are a couple different approaches:
- Use multi-shot prompting with something like guardrails to try prompting a commercial model until it works. [1]
- Use a local model with something with a final layer that steers token selection towards syntactically valid tokens [2]
[1] https://github.com/ShreyaR/guardrails
[2] "Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema" @ https://github.com/newhouseb/clownfish.
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Introducing :🤖 Megabots - State-of-the-art, production ready full-stack LLM apps made mega-easy with LangChain and FastAPI
👍 validate and correct the outputs of LLMs using guardrails
- For consistent output from vicuna 13b
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[D] Is all the talk about what GPT can do on Twitter and Reddit exaggerated or fairly accurate?
not vouching for it, but I know this is at least a thing that exists and I like the general idea: https://github.com/shreyar/guardrails
- Introducing Agents in Haystack: Make LLMs resolve complex tasks
segment-anything
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What things are happening in ML that we can't hear oer the din of LLMs?
- segment anything: https://github.com/facebookresearch/segment-anything
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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Generate new version of a living-room with specific furniture
Render a new living room using a controlnet model of your choice to keep the basic structure. Load the original living room image and look for the furniture you want to change with a Segment Anything Model to create a mask. Use that mask on the new living room to inpaint new furniture.
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How Do I read Github Pages? It is so exhausting, I always struggle, oh and I am on windows
Hello,So I am trying to run some programs, python scripts from this page: https://github.com/facebookresearch/segment-anything, and found myself spending hours without succeeding in even understanding what's is written on that page. And I think this is ultimately related to programming.
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Autodistill: A new way to create CV models
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
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How to Fine-Tune Foundation Models to Auto-Label Training Data
Webinar from last week on how to fine-tune VFMs, specifically Meta's Segment Anything Model (SAM).
What you'll need to follow along the fine-tuning walkthrough:
Images, ground-truth masks, and optionally, prompts from the Stamp Verification (StaVer) Dataset on Kaggle (https://www.kaggle.com/datasets/rtatman/stamp-verification-s...)
Download the model weights for SAM the official GitHub repo (https://github.com/facebookresearch/segment-anything)
Good understanding of the model architecture Segment Anything paper (https://ai.meta.com/research/publications/segment-anything/)
GPU infra the NVIDIA A100 should do for this fine-tuning.
Data curation and model evaluation tool Encord Active (https://github.com/encord-team/encord-active)
Colab walkthrough for fine-tuning: https://colab.research.google.com/github/encord-team/encord-...
I'd love to get your thoughts and feedback. Thank you.
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Deploying a ML model (segment-anything) to GCP - how would you do it?
I now want users to be able to use the segment-anything model (https://github.com/facebookresearch/segment-anything) in my app. It's in pytorch if that matters. How it should work is that
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The Mathematics of Training LLMs
Yeah, they are great and some of the reason (up the causal chain) for some of the work I've done! Seems really fun! <3 :))))
Facebook's Segment Anything Model I think has a lot of potentially really fun usecases. Plaintext description -> Network segmentation (https://github.com/facebookresearch/segment-anything/blob/ma...) Not sure if that's what you're looking for or not, but I love that impressing your kids is where your heart is. That kind of parenting makes me very, very, very, happy. :') <3
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How hard is it to "code" a tool based on segment-anything and Stable diffusion ?
There are some snippets of Python code on the segment-anything github readme that show how to do this. Once you have it installed you can import functions from the segment-anything module, load a segmentation model, and generate masks for input images that match the prompt of your choice. You don't need Stable Diffusion for this, but you could load it through diffusers to do things like inpaint your images using the masks.
- The less i know the better
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
backgroundremover - Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
ComfyUI-extension-tutorials
dynamic-gpt-ui - Dynamic UI generation with GPT-3 (OpenAI)
stable-diffusion-webui-Layer-Divider - Layer-Divider, an extension for stable-diffusion-webui using the segment-anything model (SAM)
truss - Assertions micro-library for Clojure/Script
Grounded-Segment-Anything - Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
ghostwheel - Hassle-free inline clojure.spec with semi-automatic generative testing and side effect detection
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"