fastembed-rs
llama.cpp
fastembed-rs | llama.cpp | |
---|---|---|
1 | 780 | |
161 | 58,425 | |
- | - | |
8.8 | 10.0 | |
6 days ago | 2 days ago | |
Rust | C++ | |
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.
fastembed-rs
-
Embeddings are a good starting point for the AI curious app developer
Yes, I use fastembed-rs[1] in a project I'm working on and it runs flawlessly. You can store the embeddings in any boring database, but for fast vector math, a vector database is recommended (e.g. the pgvector postgres extension).
[1] https://github.com/Anush008/fastembed-rs
llama.cpp
-
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
-
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
-
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
-
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
-
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?
candle_embed - A simple, CUDA or CPU powered, library for creating vector embeddings using Candle and models from Hugging Face
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
sqlite-vss - A SQLite extension for efficient vector search, based on Faiss!
gpt4all - gpt4all: run open-source LLMs anywhere
lantern_extras - Routines for generating, manipulating, parsing, importing vector embeddings into Postgres tables
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
pg_vectorize - The simplest way to orchestrate vector search on Postgres
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
ggml - Tensor library for machine learning
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧