emerging-trajectories
ollama
emerging-trajectories | ollama | |
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6 | 204 | |
57 | 64,536 | |
- | 21.5% | |
9.1 | 9.9 | |
13 days ago | 3 days ago | |
Python | Go | |
MIT License | MIT License |
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emerging-trajectories
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Large language models (e.g., ChatGPT) as research assistants
I think LLMs can do a lot more than people assume, but they need to be given the proper frameworks.
When was the last time a researcher, economist, etc. was given 10,000 papers and simply told "do some original work"? That's not how it works. Daniel (the author) provides some good examples where _streamlined_ work can happen, but again, this is pretty basic stuff.
To push this further, though, imagine LLMs that fill in frameworks... A few steps here: (1) do a lit review, (2) fill in the framework, (3) discuss what might be missing, and maybe even try and fill in the missing information.
I'm doing something like this with politics and economics (see: https://emergingtrajectories.com/) and it works generally well. I think with a ton more engineering, curating of knowledge bases, etc., one can get these LLMs to actually find some new "nuggets" of information.
Admittedly, it's very hard, but I think there's something there.
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Ask HN: Is RAG the Future of LLMs?
RAG will have a place in the LLM world, since it's a way to obtain data/facts/info for relevant queries.
Since you asked about alternatives...
(a) "World models" where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2].
(b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs the ability to run queries on said graphs. Another approach here is to build "fact databases" where, you structure observations about the world into standalone concepts/ideas/observations and reference those[4]. Again, similar to RAG but not quite RAG as we know it today.
[1] https://deepmind.google/discover/blog/alphageometry-an-olymp...
[2] https://arxiv.org/abs/2306.12672
[3] https://ec.ai/
[4] https://emergingtrajectories.com/
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Long-form factuality in large language models
For those interested in using search-augmented "reasoning", I implemented something similar in Emerging Trajectories[1], an open source package that forecasts geopolitical and economic events. We extract facts[2] from various websites (Google searches, news articles, RSS feeds) and have the LLM generate a hypothesis on a metric.
We're tracking the info forecasts to see how well this does for future events. For example, we're pitting the LLMs against each other to predict March 2024 CPI[3].
[1] https://emergingtrajectories.com/
[2] Sample code: https://github.com/wgryc/emerging-trajectories/blob/main/eme...
[3] https://emergingtrajectories.com/a/statement/28
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Ask HN: What are some actual use cases of AI Agents?
I'm working on research agents to help with economic, financial, and political research. These agents are open source (see: https://github.com/wgryc/emerging-trajectories).
The use cases are pretty straight forward and low risk:
1. Run a Google web search.
2. Query a news API.
3. Write a document based on the above, while citing sources.
Here's an example of something written yesterday, where I'm forecasting whether July 2024 will be the hottest on record: https://emergingtrajectories.com/a/forecast/74
This is working well in that the writeups are great and there are some "aha" moments, like the agent finding and referencing the The National Snow and Ice Data Center (NSIDC)... Very cool! I wouldn't have thought of it.
Then there's the part where the agent also tells me that the Oregon Department of Transportation has holidays during the summer, which doesn't matter at all.
So, YMMV, as they say... But I am more productive with these agents. I wouldn't publish anything formally without confirming and reviewing the content, though.
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Ask HN: What have you built with LLMs?
LLM agents to forecast geopolitical and economic events.
- Site: https://emergingtrajectories.com/
- GitHub repo: https://github.com/wgryc/emerging-trajectories
I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.
It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.
ollama
- Ollama v0.1.34 Is Out
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Ask HN: What do you use local LLMs for?
- Basic internet search (I start ollama CLI faster than I can start a browser - https://ollama.com)
- Formatting/changing text
- Troubleshooting code, esp. new frameworks/libs
- Recipes
- Data entry
- Organizing thoughts: High-level lists, comparison, classification, synonyms, jargon & nomenclature
- Learning esp. by analogy and example
RAG for:
- Website assistants (https://github.com/bennyschmidt/ragdoll-studio/tree/master/e...)
- Game NPCs (https://github.com/bennyschmidt/ragdoll-studio/tree/master/e...)
- Discord/Slack/forum bots (https://github.com/bennyschmidt/ragdoll-studio/tree/master/e...)
- Character-driven storytelling and creating art in a specific style for video game loading screens, background images, avatars, website art, etc. (https://github.com/bennyschmidt/ragdoll-studio/tree/master/r...)
- FLaNK-AIM Weekly 06 May 2024
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Introducing Jan
Jan goes a step further by integrating with other local engines like LM Studio and ollama.
- Ollama v0.1.33
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
# install the Ollama curl -fsSL https://ollama.com/install.sh | sh # get the llama3 model ollama pull llama2 # install the MLFlow pip install mlflow
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Create an AI prototyping environment using Jupyter Lab IDE with Typescript, LangChain.js and Ollama for rapid AI prototyping
Ollama for running LLMs locally
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Setup Llama 3 using Ollama and Open-WebUI
curl -fsSL https://ollama.com/install.sh | sh
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Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B
Streaming is not a problem (it's just a simple flag: https://github.com/wiktor-k/llama-chat/blob/main/index.ts#L2...) but I've never used voice input.
The examples show image input though: https://github.com/ollama/ollama/blob/main/docs/api.md#reque...
Maybe you can file an issue here: https://github.com/ollama/ollama/issues
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I Said Goodbye to ChatGPT and Hello to Llama 3 on Open WebUI - You Should Too
I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.
What are some alternatives?
llama.cpp - LLM inference in C/C++
gpt4all - gpt4all: run open-source LLMs anywhere
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
llama - Inference code for Llama models
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
text-generation-inference - Large Language Model Text Generation Inference
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
llama-cpp-python - Python bindings for llama.cpp
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.