tidybot
llama.cpp
tidybot | llama.cpp | |
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20 | 778 | |
490 | 57,984 | |
- | - | |
6.4 | 10.0 | |
6 months ago | 2 days ago | |
Python | C++ | |
MIT License | MIT License |
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tidybot
- TidyBot: Personalized Robot Assistance with Large Language Models
- TidyBot Personalized Robot Assistance with Large Language Models
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MemGPT: Towards LLMs as Operating Systems
>they've solved reinforcement learning?
Transformers can do Reinforcement Learning yes.
https://arxiv.org/abs/2106.01345
>they can handle continuous domains, like robot motion?
Yes they can handle it just fine.
https://tidybot.cs.princeton.edu/
https://general-pattern-machines.github.io/
https://wayve.ai/thinking/lingo-natural-language-autonomous-...
- Large Language Models as General Pattern Machines. In context, LLMs are capable of completing a wide variety of non linguistic patterns.
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SuperAlignment
Other examples(in the real world) you might find interesting.
https://tidybot.cs.princeton.edu/
- Создан робот-уборщик, который самообучается наводить порядок именно так, как нравится вам. Видео.
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Is Amazon's newly announced home robot, in development & codenamed 'Burnham', unambitious and already behind the times?
It's striking how quickly robotics is developing in 2023. Two recent demonstrations from DeepMind & a Princeton team, show relatively cheap simple robots acquiring the ability to manipulate objects in the physical world. If you're going to be developing cutting-edge robots in 2023 - surely it would plan to incorporate this?
- What are the most impressive companies trying to create real world AI (real world navigation, object manipulation etc.)?
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
What are some alternatives?
MemGPT - Create LLM agents with long-term memory and custom tools 📚🦙
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
spacy-llm - 🦙 Integrating LLMs into structured NLP pipelines
gpt4all - gpt4all: run open-source LLMs anywhere
Voyager - An Open-Ended Embodied Agent with Large Language Models
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
git-agent - Langchain Agent utilizing OpenAI Function Calls to execute Git commands using Natural Language
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
ggml - Tensor library for machine learning
dialop - DialOp: Decision-oriented dialogue environments for collaborative language agents
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM