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Ad-llama Alternatives
Similar projects and alternatives to ad-llama
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guidance
Discontinued A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance] (by microsoft)
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SurveyJS
Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
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hof
Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Constrained-Text-Generation-Studio
Code repo for "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" at the (CAI2) workshop, jointly held at (COLING 2022)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better ad-llama alternative or higher similarity.
ad-llama reviews and mentions
Posts with mentions or reviews of ad-llama.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-18.
- Show HN: A murder mystery game built on an open-source gen-AI agent framework
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Guidance: A guidance language for controlling large language models
I took a stab at making something[1] like guidance - I'm not sure exactly how guidance does it (and I'm also really curious how it would work with chat api's) but here's how my solution works.
Each expression becomes a new inference request, so it's not a single inference pass. Because each subsequent pass includes the previously inferenced text, the LLM ends up doing a lot of prefill and less decode. You only decode as much as you actually inference, the repeated passes only end up costing more in prefill (which tend to be much faster tok/s).
To work with chat tuned instruction models, you can basically still treat it as a completion model. I provide the previously completed inference text as a partially completed assistant response, e.g. with llama 2 it goes after [/INST]. You can add a bit of instruction for each inference expression which gets added to the [INST]. This approach lets you start off the inference with `{ "someField": "` for example to guarantee (at least the start of) a json response and allow you to add a little bit of instruction or context just for that field.
I didn't even try with openai api's since afaict you can't provide a partial assistant response for it to continue from. Even if you were to request a single token at a time and use logit_bias for biased sampling, I don't see how you can get it to continue a partially completed inference.
[1] https://github.com/gsuuon/ad-llama
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Simulating History with ChatGPT
Can you point me to some text-adventure engines? I'm hacking on an in-browser local llm structured inference library[1] and am trying to put together a text game demo[2] for it. It didn't even occur to me that text-adventure game engines exist, I was apparently re-inventing the wheel.
[1] https://github.com/gsuuon/ad-llama
[2] https://ad-llama.vercel.app/murder/
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Ask HN: Which programming language to learn in AI era?
Yup, I'm building a library that runs LLM's in browser with tagged template literals: https://github.com/gsuuon/ad-llama
I think it has fundamental DX benefits over python for complex prompt chaining (or I wouldn't be building it!) Even still -- if their focus is purely on AI, python is still the better choice starting from scratch. The python AI ecosystem has many more libraries, stack overflow answers, tutorials, etc available.
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Show HN: LLMs can generate valid JSON 100% of the time
Generating an FSM over the vocabulary is a really interesting approach to guided sampling! I'm hacking on a structured inference library (https://github.com/gsuuon/ad-llama) - I also tried to add a vocab preprocessing step to generate a valid tokens mask (just with regex or static strings initially) but discovered that doing so would cause unlikely / unnatural tokens to be masked rather than the token which represents the natural encoding given the existing sampled tokens.
Given the stateful nature of tokenizers, I decided that trying to preprocess the individual token ids was a losing battle. Even in the simple case of whitespace - tokenizer merges can really screw up generating a static mask, e.g. we expect a space next, but a token decodes to 'foo', but is actually a '_foo' and would've decoded with a whitespace if it were following a valid pair. When I go to construct the static vocab mask, it would then end up matching against 'foo' instead of ' foo'.
How did you work around this for the FSM approach? Does it somehow include information about merges / whitespace / tokenizer statefulness?
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A note from our sponsor - SurveyJS
surveyjs.io | 4 May 2024
Stats
Basic ad-llama repo stats
6
47
8.9
27 days ago
gsuuon/ad-llama is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of ad-llama is TypeScript.
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