outlines
faker
outlines | faker | |
---|---|---|
33 | 58 | |
5,799 | 11,823 | |
11.0% | 2.2% | |
9.7 | 9.7 | |
6 days ago | 4 days ago | |
Python | TypeScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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outlines
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Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens
> [2]: https://github.com/outlines-dev/outlines?tab=readme-ov-file#...
It's interesting as speech recognition has become more popular than ever through services like Alexa, and other iot devices support for OS speech recognition
Unfortunately most implementations (especially those that are iot focused) don't have very important features for robust speech recognition.
1. Ability to enable and disable a grammar
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Show HN: LLM-powered NPCs running on your hardware
[4] https://github.com/outlines-dev/outlines/tree/main
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Advanced RAG with guided generation
The next step is defining how to guide generation. For this step, we'll use the Outlines library. Outlines is a library for controlling how tokens are generated. It applies logic to enforce schemas, regular expressions and/or specific output formats such as JSON.
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Anthropic's Haiku Beats GPT-4 Turbo in Tool Use
No benchmarks, just my anecdotal experience trying to get local LLM's to respond with JSON. The method above works for my use case nearly 100% of the time. Other things I've tried (e.g. `outlines`[0]) are really slow or don't work at all. Would love to hear what others have tried!
0 - https://github.com/outlines-dev/outlines
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Show HN: Chess-LLM, using constrained-generation to force LLMs to battle it out
As I was playing with the Outlines library (https://outlines-dev.github.io/outlines/), I discussed with my friend Maxime how funny it would be if we set up a way to pair LLMs in chess matches till one wins. The first time I tried it, it required substantial prompt engineering to get some of those LLMs to propose valid moves. Large language models can mostly stay focused and even play rather well; see https://news.ycombinator.com/item?id=37616170 for example. However small language models aren't as easy to convince.
Some of those LLMs have seen very little chess notation and so after the first few opening moves there aren't any valid tactics, let alone strategy, so they would end up either repeating the same move, or hallucinate moves that are not valid (Kxe5, but there would be a queen on e5!)
Then Outlines came along and we could force them to pick valid moves with little cost! Maxime worked super fast and got a first version of this idea as a gradio space.
I think it is pretty fun to see the (mostly terrible, but otherwise valid) chess that those LLMs play. Maybe it will even be instructive to how we can create small LLMs that can play much better than the ones on the leaderboard.
Anyway, you can check it out here:
https://huggingface.co/spaces/mlabonne/chessllm
What is interactive about it: you can pick the LLMs from available models on HuggingFace (within reason, small LLMs are preferable so that the space does not crash) or push one of your own small models to HF and have it fight with others. At the end of the game the leaderboard is updated.
Hope you find it fun!
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Show HN: Prompts as (WASM) Programs
> The most obvious usage of this is forcing a model to output valid JSON
Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?
0. https://github.com/outlines-dev/outlines
1. https://github.com/guidance-ai/guidance
2. https://github.com/sgl-project/sglang
- Show HN: Fructose, LLM calls as strongly typed functions
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Unlocking the frontend – a call for standardizing component APIs pt.2
And I think “just” Markdown doesn’t quite cut it for safe guidance. For example: directly generating content for your components. But I’m really excited about tooling like outlines appearing, with a greater focus on guided generation for structured data. Because this is often what we actually need!
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Ask HN: What are some actual use cases of AI Agents?
It's pretty easy to force a locally running model to always output valid JSON: when it gives you probabilities for the next tokens, discard all tokens that would result in invalid JSON at that point (basically reverse parsing), and then apply the usual techniques to pick the completion only from the remaining tokens. You can even validate against a JSON schema that way, so long as it is simple enough.
There are a bunch of libraries for this already, e.g.: https://github.com/outlines-dev/outlines
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Launch HN: AgentHub (YC W24) – A no-code automation platform
https://github.com/outlines-dev/outlines/blob/7fae436345e621... squares with my experience using LLMs for anything real
sequence = generator("Alice had 4 apples and Bob ate 2. Write an expression for Alice's apples:")
faker
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Easily create mock data for unit tests 🧪
Instead of manually having to think of defaults for your interface properties, you could use Faker.
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Leveling up your custom fake data with Faker.js
If you think you have something which other Faker users would find useful, you can contribute it to Faker! See the contributing guide, you can create an issue or a pull request.
- Front-End Prototyping - Mock JSON Data Provider
- Show HN: Buyidentities.com
- Show HN: Generate JSON mock data for testing/initial app development
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Web workers in ReactJs
First, we create a react project, and then we use a service faker to create 25000 user records.
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Component Testing with Cypress and Reactjs
Use faker.js to generate random values for your mock data.
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Sveltekit Twitter Clone starter made with Lucia Auth, Prisma and Faker.js
I built a starter project for developing a social media app with Sveltekit, Lucia Auth, Prisma and Faker.js. Uses sqlite for prototyping but can easily be changed to Postgres or MySQL.
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How do you get realistic data in your staging databases?
Don’t even need chatGPT or json files! There are libraries like faker that’ll do it on the fly for you!
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C++ Faker library
I am currently working with Typescript as well as C++ and I enjoyed using FakerJS library for testing data. I've checked for C++ libraries and I haven't found anything that could be used for my basic needs (like generating emails, passwords, names, uuids or lorem words) so I just started my own project with idea to deliver such library to C++ developers.
What are some alternatives?
guidance - A guidance language for controlling large language models.
falso - All the Fake Data for All Your Real Needs 🙂
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
Faker.js - What really happened with Aaron Swartz?
json-schema-spec - The JSON Schema specification
yup-schema-faker - Fake data generator for yup
Constrained-Text-Genera
casual - Fake data generator for javascript
torch-grammar
plop - Consistency Made Simple
langroid - Harness LLMs with Multi-Agent Programming
Prisma - Next-generation ORM for Node.js & TypeScript | PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, MongoDB and CockroachDB