ggml
gpt4all
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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/
gpt4all
- Show HN: I made an app to use local AI as daily driver
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Ollama Python and JavaScript Libraries
I don’t know if Ollama can do this but https://gpt4all.io/ can.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/
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WyGPT: Minimal mature GPT model in C++
The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?
Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)
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LibreChat
Check https://github.com/nomic-ai/gpt4all instead.
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OpenAI Negotiations to Reinstate Altman Hit Snag over Board Role
"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."
"extract the reusable functions out of this bash script"
"write me a cfssl command to generate a intermediate CA"
"What is the regex for _____"
"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."
etc etc etc
If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.
Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.
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Darbe uzdraude naudotis CHATGPT: ar cia normalu?
offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html
- GPT4All: An ecosystem of open-source on-edge large language models - by Nomic AI
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Why use OpenAI's ChatGPT3.5 online service, if you can instead host your own local llama?
Take a look at https://gpt4all.io, their docs are pretty awesome
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Ask HN: Are you using a local LLM? If yes, what for?
I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.
https://github.com/nomic-ai/gpt4all
What are some alternatives?
llama.cpp - LLM inference in C/C++
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
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
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)