jsonformer
jikkou
| jsonformer | jikkou | |
|---|---|---|
| 25 | 5 | |
| 3,868 | 163 | |
| - | 6.1% | |
| 5.4 | 9.5 | |
| 3 months ago | 1 day ago | |
| Jupyter Notebook | Java | |
| MIT License | Apache License 2.0 |
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jsonformer
- Forcing AI to Follow a Specific Answer Pattern Using GBNF Grammar
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Refact LLM: New 1.6B code model reaches 32% HumanEval and is SOTA for the size
- Tools like jsonformer https://github.com/1rgs/jsonformer are not possible with OpenAIs API.
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Show HN: LLMs can generate valid JSON 100% of the time
How does this compare in terms of latency, cost, and effectiveness to jsonformer? https://github.com/1rgs/jsonformer
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Ask HN: Explain how size of input changes ChatGPT performance
You're correct with interpreting how the model works wrt it returning tokens one at a time. The model returns one token, and the entire context window gets shifted right by one to for account it when generating the next one.
As for model performance at different context sizes, it's seems a bit complicated. From what I understand, even if models are tweaked (for example using the superHOT RoPE hack or sparse attention) to be able to use longer contexts, they still have to be fined tuned on input of this increased context to actually utilize it, but performance seems to degrade regardless as input length increases.
For your question about fine tuning models to respond with only "yes" or "no", I recommend looking into how the jsonformers library works: https://github.com/1rgs/jsonformer . Essentially, you still let the model generate many tokens for the next position, and only accept the ones that satisfy certain criteria (such as the token for "yes" and the token for "no".
You can do this with openAI API too, using tiktoken https://twitter.com/AAAzzam/status/1669753722828730378?t=d_W... . Be careful though as results will be different on different selections of tokens, as "YES", "Yes", "yes", etc are all different tokens to the best of my knowledge
- A framework to securely use LLMs in companies – Part 1: Overview of Risks
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LLMs for Schema Augmentation
From here, we just need to continue generating tokens until we get to a closing quote. This approach was borrowed from Jsonformer which uses a similar approach to induce LLMs to generate structured output. Continuing to do so for each property using Replit's code LLM gives the following output:
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Doesn't a 4090 massively overpower a 3090 for running local LLMs?
https://github.com/1rgs/jsonformer or https://github.com/microsoft/guidance may help get better results, but I ended up with a bit more of a custom solution.
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“Sam altman won't tell you that GPT-4 has 220B parameters and is 16-way mixture model with 8 sets of weights”
I think function calling is just JSONformer idk: https://github.com/1rgs/jsonformer
- Inference Speed vs. Quality Hacks?
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Best bet for parseable output?
jsonformer: https://github.com/1rgs/jsonformer
jikkou
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Jikkou 0.31.0 is released! Use The Declarative Power of REST APIs to manage Apache Kafka®
Jikkou is an open-source product designed to swiftly and efficiently manage, automate and provision all the assets of your data streaming platform.
- FLaNK Stack Weekly 29 may 2023
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Why is Managing Kafka Topics Still Such a Pain? Introducing Jikkou!
GitHub: https://github.com/streamthoughts/jikkou
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Creating topics and connectors in Kafka in a Kubernetes cluster
Maybe this CLI can help you: Jikkou. This is a project I'm working on to manage Kafka Topics, ACLs, Quotas and so on. The purpose of this tool is to be integrated into a CI/CD pipeline.
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KafkaSpecs is now Jikkou ! Yet another command-line tool to help you automate the management of the configurations that live on your Apache Kafka clusters
Release v0.6.0 is now available! https://github.com/streamthoughts/jikkou
What are some alternatives?
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spring-boot-migrator - Spring Boot Migrator (SBM) is a tool for automated code migrations to upgrade or migrate to Spring Boot
evadb - Database system for AI-powered apps
kafka-native - Kafka broker compiled to native using Quarkus and GraalVM.