bloomz.cpp
ggml
bloomz.cpp | ggml | |
---|---|---|
4 | 69 | |
806 | 9,863 | |
- | - | |
6.0 | 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.
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.
bloomz.cpp
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My largest ever quants, GPT 3 sized! BLOOMZ 176B and BLOOMChat 1.0 176B
Possibly. There's a llama.cpp fork called bloomz.cpp but it's not been updated in 2 months. So it's not going to support any of the fancy new quantisation methods, performance improvements, GPU acceleration, etc.
- Bloomz.cpp: C++ implementation for BLOOM models
- Bloomz.cpp: Run multilingual BLOOM model with C++
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[N] bloomz.cpp: Run any BLOOM-like model in pure C++
bloomz.cpp allows running inference of BLOOM-like models in pure C/C++ (inspired by llama.cpp). It supports all models that can be loaded with BloomForCausalLM.from_pretrained(). For example, you can achieve 16 tokens per second on a M1 Pro.
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?
libvips - A fast image processing library with low memory needs.
llama.cpp - LLM inference in C/C++
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
lm-evaluation-harness - A framework for few-shot evaluation of language models.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
vosk - VOSK Speech Recognition Toolkit
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
ggml - Tensor library for machine learning
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
StableLM - StableLM: Stability AI Language Models