BrainChulo
guidance
BrainChulo | guidance | |
---|---|---|
10 | 89 | |
140 | 12,248 | |
0.7% | - | |
9.0 | 9.5 | |
7 months ago | 9 months ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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BrainChulo
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Alternative to LangChain for open LLMs?
On BrainChulo, we’re going 100% guidance mode, see for instance an implementation of Chain of Thoughts on top of a thin guidance wrapper: https://github.com/ChuloAI/BrainChulo/blob/main/app/guidance_tooling/guidance_agent/agent.py
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Running local LLM for info retrieval of technical documents
Awesome resource! If I may suggest that you'd add one, some friends and I are working on data retrieval with llm project as well, with our differentiating marker being that we are trying to implement guidance in order to improve the agent efficiency. If you guys wanna take a look :) https://github.com/ChuloAI/BrainChulo
- LlamaCPP and LangChain Agent Quality
- Training a 13B LLaMA on information from documents.
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Chat with Documents using Open source LLMs
Plug: https://github.com/iGavroche/BrainChulo - BrainChulo currently works on top of Ooba but uses its own UI interface. Its first goal is to provide a production-level way to do Retrieveal Augmentation on Open Source LLMs via vector stores and good prompt engineering.
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What features would everyone like to see in oog?
Regarding this, I've joined a project that is doing some nice progress on this front. Still WIP but we're getting there, checkout BrainChulo :)
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7B models use with Langchainn for Chatbox importing of txt or pdf's
This is exactly what BrainChulo aims to do. You should check it out: https://github.com/CryptoRUSHGav/BrainChulo/ and feel free to drop on the discord to give us your feedback, your use-case, or if you need help getting started.
- [Local Llama] Aggiunta di memoria a lungo termine a LLM personalizzati: domiamo Vicuna insieme!
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adding models to oobabooga
The download script is broken. I posted a working version on my repo: https://github.com/CryptoRUSHGav/BrainChulo
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Adding Long-Term Memory to Custom LLMs: Let's Tame Vicuna Together!
I'm hoping that many of you brilliant people can join me in our common quest to add long-term memory to our favorite camelid, Vicuna. The repository is called BrainChulo, and it's just waiting for your contributions.
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?
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
lmql - A language for constraint-guided and efficient LLM programming.
outlines - Structured Text Generation
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
long_term_memory - A gradio web UI for running Large Language Models like GPT-J 6B, OPT, GALACTICA, LLaMA, and Pygmalion.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
gpt-llama.cpp - A llama.cpp drop-in replacement for OpenAI's GPT endpoints, allowing GPT-powered apps to run off local llama.cpp models instead of OpenAI.
llama-cpp-python - Python bindings for llama.cpp
ChatALL - Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, Claude, ChatGLM, MOSS, 讯飞星火, 文心一言 and more, discover the best answers
langchainrb - Build LLM-powered applications in Ruby