alpaca.cpp
ggml
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alpaca.cpp | ggml | |
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94 | 69 | |
9,878 | 9,566 | |
- | - | |
9.4 | 9.8 | |
about 1 year ago | 6 days ago | |
C | C | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
alpaca.cpp
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LLaMA Now Goes Faster on CPUs
Where's the 30B-in-6GB claim? ^FGB in your GH link finds [0] which is neither by jart nor by ggerganov but by another user who promptly gets told to look at [1] where Justine denies that claim.
[0] https://github.com/antimatter15/alpaca.cpp/issues/182
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Is there potential to short NVDA?
You can just download the language model, dude!!! Everyone doesn’t need to make their own and the open source models literally get better every day.
- [Oobabooga] Alpaca.cpp est extrêmement simple à travailler.
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Hollywood’s Screenwriters Are Right to Fear AI
Alpaca
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Square Enix’s AI Tech Demo Is a Staggering Failure
Square could have also trained a more specific data source for their NLP, very similar to Alpaca. Alpaca was trained from interactions from a larger dataset. So while it isn't as smart, it's still able to understand instructions and act upon them.
- [Singularity] Ich bin Alpaka 13B - Frag mich alles
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Alpaca Vs. Final Jeopardy
The model I found was in 8 parts. The alpaca.cpp chat client (chat.cpp) needs to be modified to run the 8 part model, documented here: https://github.com/antimatter15/alpaca.cpp/issues/149
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LocalAI: OpenAI compatible API to run LLM models locally on consumer grade hardware!
try the instructions on this github repo https://github.com/antimatter15/alpaca.cpp, its not the best one but I was able to run this model on my linux machine with 16GB memory, I think its a good starting point.
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What educational materials do you think would be most useful during/after collapse?
Doesn't run offline. If you're running something without a beefy-ish GPU, there's https://github.com/antimatter15/alpaca.cpp .
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ChatGPT Reignited My Passion For Coding
Ye, atm. toying with alpaca 7B/13B in a local install.
ggml
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LLMs on your local Computer (Part 1)
git clone https://github.com/ggerganov/ggml cd ggml mkdir build cd build cmake .. make -j4 gpt-j ../examples/gpt-j/download-ggml-model.sh 6B
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GGUF, the Long Way Around
Cool. I was just learning about GGUF by creating my own parser for it based on the spec https://github.com/ggerganov/ggml/blob/master/docs/gguf.md (for educational purposes)
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Ask HN: People who switched from GPT to their own models. How was it?
If you don't care about the details of how those model servers work, then something that abstracts out the whole process like LM Studio or Ollama is all you need.
However, if you want to get into the weeds of how this actually works, I recommend you look up model quantization and some libraries like ggml[1] that actually do that for you.
[1] https://github.com/ggerganov/ggml
- GGUF File Format
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Google just shipped libggml from llama-cpp into its Android AICore
Because the library is called ggml, but it supports gguf.
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Q-Transformer
Apparently this guy like a bunch of others like https://github.com/ggerganov/ggml are implementing transformers from papers for people that want them. Pretty cool.
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[P] Inference Vision Transformer (ViT) in plain C/C++ with ggml
You can access it here: https://github.com/staghado/vit.cpp It has been added to the ggml library on GitHub: https://github.com/ggerganov/ggml
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Falcon 180B Released
https://github.com/ggerganov/ggml
One note is that prompt ingestion is extremely slow on CPU compared to GPU. So short prompts are fine (as tokens can be streamed once the prompt is ingested), but long prompts feel extremely sluggish.
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Stable Diffusion in pure C/C++
I did a quick run under profiler and on my AVX2-laptop the slowest part (>50%) was matrix multiplication (sgemm).
In current version of GGML if OpenBLAS is enabled, they convert matrices to FP32 before running sgemm.
If OpenBLAS is disabled, on AVX2 plaftorm they convert FP16 to FP32 on every FMA operation, which even worse (due to repetition). After that, both ggml_vec_dot_f16 and ggml_vec_dot_f32 took first place in profiler.
Source: https://github.com/ggerganov/ggml/blob/master/src/ggml.c#L10...
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
For those getting started, the easiest one click installer I've used is Nomic.ai's gpt4all: https://gpt4all.io/
This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama.cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J models. It also has API/CLI bindings.
I just saw a slick new tool https://ollama.ai/ that will let you install a llama2-7b with a single `ollama run llama2` command that has a very simple 1-click installer for Apple Silicon Mac (but need to build from source for anything else atm). It looks like it only supports llamas OOTB but it also seems to use llama.cpp (via Go adapter) on the backend - it seemed to be CPU-only on my MBA, but I didn't poke too much and it's brand new, so we'll see.
For anyone on HN, they should probably be looking at https://github.com/ggerganov/llama.cpp and https://github.com/ggerganov/ggml directly. If you have a high-end Nvidia consumer card (3090/4090) I'd highly recommend looking into https://github.com/turboderp/exllama
For those generally confused, the r/LocalLLaMA wiki is a good place to start: https://www.reddit.com/r/LocalLLaMA/wiki/guide/
I've also been porting my own notes into a single location that tracks models, evals, and has guides focused on local models: https://llm-tracker.info/
What are some alternatives?
gpt4all - gpt4all: run open-source LLMs anywhere
llama.cpp - LLM inference in C/C++
alpaca-lora - Instruct-tune LLaMA on consumer hardware
coral-pi-rest-server - Perform inferencing of tensorflow-lite models on an RPi with acceleration from Coral USB stick
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llm - An ecosystem of Rust libraries for working with large language models
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
StableLM - StableLM: Stability AI Language Models