airoboros
guidance
airoboros | guidance | |
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8 | 89 | |
940 | 12,248 | |
- | - | |
8.7 | 9.5 | |
about 2 months ago | 9 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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airoboros
- TinyLlama project aims to pretrain a 1.1B Llama model on 3T tokens
- Airoboros: Customizable implementation of the self-instruct paper
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airoboros (tool) overhaul
Just wanted to drop a note that I overhauled the airoboros tool not the models to have most of the prompts I've been using to build the datasets, plus a couple extras.
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(2/2) May 2023
airoboros: using large language models to fine-tune large language models (https://github.com/jondurbin/airoboros)
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Airoboros [7B/13B]
This is a fine-tuned LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros
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airobors-13b - 98% eval vs gpt-3.5-turbo
I used airoboros, a python tool I wrote, to generate the synthetic instruction response pairs, and included a jailbreak prompt to attempt to bypass OpenAI censorship. This is the only dataset used to fine-tune the model.
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[P] airoboros 7b - instruction tuned on 100k synthetic instruction/responses
This is a 7b parameter, fine-tuned on 100k synthetic instruction/response pairs generated by gpt-3.5-turbo using my version of self-instruct airoboros
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[P] airoboros: a rewrite of self-instruct/alpaca synthetic prompt generation
GitHub Repo
guidance
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Guidance: A guidance language for controlling large language models
This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.
https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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Llama: Add Grammar-Based Sampling
... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.
https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...
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Introducing TypeChat from Microsoft
Here's one thing I don't get.
Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...
...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.
This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance
But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?
I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].
[0]https://github.com/microsoft/guidance
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AutoChain, lightweight and testable alternative to LangChain
LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.
So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.
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Structured Output from LLMs (Without Reprompting!)
I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201
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/r/guidance is now a subreddit for Guidance, Microsoft's template language for controlling language models!
Let's have a subreddit about Guidance!
- Is there a UI that can limit LLM tokens to a preset list?
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Any suggestions for an open source model for parsing real estate listings?
You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)
What are some alternatives?
WizardLM - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder and WizardMath
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
TinyLlama - The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
lmql - A language for constraint-guided and efficient LLM programming.
WizardVicunaLM - LLM that combines the principles of wizardLM and vicunaLM
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
datablations - Scaling Data-Constrained Language Models
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
chain-of-thought-hub - Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
llama-cpp-python - Python bindings for llama.cpp
gorilla - Gorilla: An API store for LLMs
langchainrb - Build LLM-powered applications in Ruby