NeMo-Guardrails
basaran
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NeMo-Guardrails | basaran | |
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13 | 22 | |
3,338 | 1,281 | |
7.9% | - | |
9.9 | 10.0 | |
6 days ago | 3 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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NeMo-Guardrails
- NeMO Guardrails from Nvidia
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Run and create custom ChatGPT-like bots with OpenChat
- https://github.com/NVIDIA/NeMo-Guardrails/
- LangChain: The Missing Manual
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The Dual LLM pattern for building AI assistants that can resist prompt injection
Here's "jailbreak detection", in the NeMo-Guardrails project from Nvidia:
https://github.com/NVIDIA/NeMo-Guardrails/blob/327da8a42d5f8...
I.e. they ask the llm if the prompt will break the llm. (I believe that more data /some evaluation on how well this performs is intended to be released. Probably fair to call this stuff "not battle tested".)
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How To Setup a Model With Guardrails?
I have been playing around with some models locally and creating a discord bot as a fun side project, and I wanted to setup some guardrails on inputs / outputs of the bot to make sure that it isn't violating any ethical boundaries. I was going to use Nvidia's Nemo guardrails, but they only support openai currently. Are there any other good ways to control inputs?
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
Thanks, I hadn't seen those. I did find https://github.com/NVIDIA/NeMo-Guardrails earlier but haven't looked into it yet.
I'm not sure it solves the problem of restricting the information it uses though. For example, as a proof of concept for a customer, I tried providing information from a vector database as context, but GPT would still answer questions that were not provided in that context. It would base its answers on information that was already crawled from the customer website and in the model. That is concerning because the website might get updated but you can't update the model yourself (among other reasons).
- How do we prevent prompt injection in a GPT API app?
- Nvidia NeMo Guardrails – open-source guardrails to conversational systems
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Should LangChain be used in Prod?
you can use guard rails with langchain - https://github.com/NVIDIA/NeMo-Guardrails
basaran
- OpenLLM
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Langchain and self hosted LLaMA hosted API
What are the current best "no reinventing the wheel" approaches to have Langchain use an LLM through a locally hosted REST API, the likes of Oobabooga or hyperonym/basaran with streaming support for 4-bit GPTQ?
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Run and create custom ChatGPT-like bots with OpenChat
Disclaimer: I am curating LLM-tools on github [1]
A few thoughts:
* allow for custom endpoint URLs, this way people can use open source LLMs with a fake openAI API backend like basaran[2] or llama-api-server[3]
* look into better embedding methods for info-retrieval like InstructorEmbeddings or Document Summary Index
* Don't use a single embedding per content item, use multiple to increase retrieval quality
1 https://github.com/underlines/awesome-marketing-datascience/...
2 https://github.com/hyperonym/basaran
3 https://github.com/iaalm/llama-api-server
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1-Jun-2023
open-source alternative to the OpenAI text completion API (https://github.com/hyperonym/basaran)
- Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
- Basaran is an open-source alternative to the OpenAI text completion API
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Ask HN: What's the best self hosted/local alternative to GPT-4?
Guanaco-65B[0] using Basaran[1] for your OpenAI compatible API. You can use any ChatGPT front-end which lets you change the OpenAI endpoint URL.
[0] An fp4 finetune of LLaMA-30B by Tim Dettmers
[1] https://github.com/hyperonym/basaran
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Are all the finetunes stupid?
For lm-eval, I think you'd either need to take GPTQ's inference script and shim it into a model: https://github.com/EleutherAI/lm-evaluation-harness/tree/master/lm_eval/models or you might be able to use a project like https://github.com/hyperonym/basaran and then you could use the gpt3 model...
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Using the API in Node
There are also: - Basaran repo: "Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models". "...Compatibility with OpenAI API and client libraries..."; - llama-cpp-python repo: "Simple Python bindings for @ggerganov's llama.cpp library...". "...OpenAI-like API...".
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Researcher looking for help with how to prepare a finetuning dataset for models like Bloomz and Cerebras-GPT
I want to start with a totally freely available model, so again, that excludes things like LLaMA where the weights are only available through a wait list. The two models that most get my attention and (I think, and hope) fit my criteria of open availability are Cerebras-GPT (13b) and Bloomz (7b). The tools to process and fine-tune that seem most feasible to me, from my limit knowledge, are xturing and basaran.
What are some alternatives?
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
text-generation-inference - Large Language Model Text Generation Inference
langchainrb - Build LLM-powered applications in Ruby
openai-chatgpt-opentranslator - Python command that uses openai to perform text translations
guidance - A guidance language for controlling large language models.
AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
lmql - A language for constraint-guided and efficient LLM programming.
llm-foundry - LLM training code for Databricks foundation models
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
pgvector - Open-source vector similarity search for Postgres