datablations
guidance
datablations | guidance | |
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6 | 89 | |
290 | 12,248 | |
3.8% | - | |
6.9 | 9.5 | |
about 1 month ago | 9 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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datablations
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Gemini is only 1x Chinchilla, so it undertrained for production
1x chinchilla means it's not really undertrained but that more could be squeezed without excessive difficulty https://arxiv.org/abs/2305.16264
- Can LLMs learn from a single example?
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Chinchilla’s Death
You might want to give a read to "Scaling Data-Constrained Language Models" [1]. They basically generalized the Chinchilla scaling law by investigating behavior on multi-epoch runs.
[1] https://arxiv.org/abs/2305.16264
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RWKV Pile+ seems to be training on far more tokens than any LLM ever has
I would imagine that there is a lot of overlap, yeah. That said, training on repeated data does seem to be effective at this level.
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(2/2) May 2023
Scaling Data-Constrained Language Models (https://arxiv.org/abs/2305.16264)
- How to Keep Scaling Large Language Models when Data Runs Out? A New AI Research Trains 400 Models with up to 9B Parameters and 900B Tokens to Create an Extension of Chinchilla Scaling Laws for Repeated Data
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?
TinyLlama - The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
airoboros - Customizable implementation of the self-instruct paper.
lmql - A language for constraint-guided and efficient LLM programming.
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
llama-cpp-python - Python bindings for llama.cpp
chathub - All-in-one chatbot client
langchainrb - Build LLM-powered applications in Ruby