benchmark
llama.cpp
| benchmark | llama.cpp | |
|---|---|---|
| 3 | 780 | |
| 789 | 57,984 | |
| 3.3% | - | |
| 9.8 | 10.0 | |
| 3 days ago | 6 days ago | |
| Python | C++ | |
| BSD 3-clause "New" or "Revised" License | 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.
benchmark
-
PyTorch Primitives in WebGPU for the Browser
>What's a fair benchmark?
the absolute golden benchmarks are https://github.com/pytorch/benchmark
- [R] AMD Instinct MI25 | Machine Learning Setup on the Cheap!
-
[R] PyTorch | Budget GPU Benchmarking
TorchBench (https://github.com/pytorch/benchmark) is used by PyTorch core developers to test performance across a wide variety of models. I've never used it personally though.
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?
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
potatogpt - Pure Typescript, dependency free, ridiculously slow implementation of GPT2 for educational purposes
gpt4all - gpt4all: run open-source LLMs anywhere
opov - Operator Overloading for Typescript with Tagged Template Literals
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
shumai - Fast Differentiable Tensor Library in JavaScript and TypeScript with Bun + Flashlight
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
requests-wasm-polyfill - Drop-in replacement for the requests library for wasm python
ggml - Tensor library for machine learning
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM