scyther-proof
guardrails
| scyther-proof | guardrails | |
|---|---|---|
| - | 13 | |
| 13 | 3,361 | |
| - | 6.4% | |
| 0.0 | 9.9 | |
| almost 9 years ago | 4 days ago | |
| Isabelle | Python | |
| LicenseRef-GPL | 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.
scyther-proof
We haven't tracked posts mentioning scyther-proof yet.
Tracking mentions began in Dec 2020.
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?
-
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?
-
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.
-
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
-
[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
What are some alternatives?
faceted - Secure Information Flow
lmql - A language for constraint-guided and efficient LLM programming.
smith-cli - This is a command line tool for interacting with https://smith.st
GPTCache - Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
liboath-hs - Haskell bindings to the liboath library.
JARVIS - JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
openssh-protocol - Haskell implementation of openssh protocol primitives.
truss - Assertions micro-library for Clojure/Script
lmonad - Information Flow Control for Haskell applications
dynamic-gpt-ui - Dynamic UI generation with GPT-3 (OpenAI)
planb-token-introspection - PlanB Token Introspection
ghostwheel - Hassle-free inline clojure.spec with semi-automatic generative testing and side effect detection