RustBook
ollama
| RustBook | ollama | |
|---|---|---|
| 2 | 198 | |
| 2,408 | 62,615 | |
| - | 19.1% | |
| 4.9 | 9.9 | |
| 17 days ago | 3 days ago | |
| Rust | Go | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RustBook
-
LLMs and Programming in the first days of 2024
To rephrase it a little bit.
Much of programming, coding and developing is done by a person who is a knowledge worker and writes code. A good proportion of code to be written, will be written just once and never again. The one-off code snippet will stay in a file collecting dust forever. There is no point in trying to remember it in the first place, because without constant repetition of using it, it will be forgotten.
LLMs can help us focus our knowledge where it really matters, and discard a lot of the ephemeral stuff. That means that we can be more of knowledge workers and less of coders. I will push it even further and state that we will become more of knowledge workers and less of coders until we will be, eventually and gradually, just knowledge workers. We will need to know about algorithms, algorithmic complexity, abstractions and stuff like that.
We will need to know subjects like that Rust book [1] writes about.
[1]https://github.com/QMHTMY/RustBook/tree/main/books
-
Data structures/algorithms course in rust?
This seems to be exactly what I am looking for, but it's in chinese.
ollama
-
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
-
Create an AI prototyping environment using Jupyter Lab IDE with Typescript, LangChain.js and Ollama for rapid AI prototyping
Ollama for running LLMs locally
-
Setup Llama 3 using Ollama and Open-WebUI
curl -fsSL https://ollama.com/install.sh | sh
-
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
-
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.
-
Let’s build AI-tools with the help of AI and Typescript!
Ollama for running LLMs locally
-
One LLaMa to rule them all
There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁
-
How to Run Llama 3 Locally with Ollama and Open WebUI
That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.
- Ollama: Acknowledge the work done by Georgi and team
-
Mixtral 8x22B
easiest is probably with ollama [0]. I think the ollama API is OpenAI compatible.
[0]https://ollama.com/
What are some alternatives?
Rust - All Algorithms implemented in Rust
llama.cpp - LLM inference in C/C++
too-many-lists - Learn Rust by writing Entirely Too Many linked lists
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)