localLLM_langchain
guidance
localLLM_langchain | guidance | |
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2 | 89 | |
46 | 12,248 | |
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3.6 | 9.5 | |
12 months ago | 9 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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localLLM_langchain
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local LLM agent?
I was able to make a dumb bot (react framework) using wizardlm 13b and 30b (1.1 for 13b) (using something similar to this: https://github.com/QuangBK/localLLM_langchain/blob/main/localLLM_agent.ipynb, without langchain. But it seems like it really struggles with following the correct format when giving the output. Sometimes, its thought is to search, but instead, it parses a web page. I have tried using natural language and json format output. I've tweeked the prompt several times to varying degrees of success. I've played with the temperature with some success.
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I made a simple agent demo with Guidance and wizard-mega-13B-GPTQ, feel quite promising.
Also, you can compare Langchain and Guidance here: Guidance Agent and Langchain Agent
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?
guidance - A guidance language for controlling large language models.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
llama-cpp-python - Python bindings for llama.cpp
lmql - A language for constraint-guided and efficient LLM programming.
localLLM_guidance - Local LLM ReAct Agent with Guidance
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
local-guidance - A guidance language for controlling large language models.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
langchainrb - Build LLM-powered applications in Ruby
llama.cpp - LLM inference in C/C++
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.