rllama
ggml
rllama | ggml | |
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7 | 69 | |
522 | 10,002 | |
- | - | |
6.2 | 9.8 | |
4 months ago | 6 days ago | |
Rust | C | |
GNU Affero General Public License v3.0 | 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.
rllama
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Ask HN: Who wants to be hired? (July 2023)
Location: San Francisco
Remote: No preference, as long as I don't have to move far from Bay Area
Willing to relocate: No
Technologies: C, Rust, Golang, Haskell, Lisp, Python, Lua, OpenGL, SQLite3, JavaScript, PostgreSQL, AWS EC2, S3, ECS, Batch.
Resume: https://www.linkedin.com/in/mikjuola
Email: [email protected]
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I've been working at the Bay Area since 2015, most recently at Pinterest. At work, I've done big data pipelines, designed some batch job systems, computing metrics, handling billing APIs, lots of Python, Go and Java and working with AWS, i.e. backend and data engineer stuff.
But I'm trying to look for work that's more in line with what I do on my free time: Challenging low-level C or Rust programming, machine learning implementations (see e.g. this thing I made https://github.com/Noeda/rllama/, graphics programming or research-type work, uncommon programming languages.
If you scroll through my random crap repositories you can see what kind of things I'm interested in: https://github.com/Noeda?tab=repositories
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State-of-the-art open-source chatbot, Vicuna-13B, just released model weights
No, my project is called rllama. No relation to GGML. https://github.com/Noeda/rllama
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Where can I learn more about SIMD, CPU intrinsics and the like in the context of Rust?
I have seen some Rust attempts as well such as https://github.com/Noeda/rllama/ but they are still way behind the C++ ones. This seems like an interesting space to get into.
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Show HN: Alpaca.cpp – Run an Instruction-Tuned Chat-Style LLM on a MacBook
I ran it on a 128 RAM machine with a Ryzen 5950X. It's not fast, 4 seconds per token. But it's just about fits without swapping. https://github.com/Noeda/rllama/
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Llama.rs – Rust port of llama.cpp for fast LLaMA inference on CPU
I've counted three different Rust LLaMA implementations on r/rust subreddit this week:
https://github.com/Noeda/rllama/ (pure Rust+OpenCL)
https://github.com/setzer22/llama-rs/ (ggml based)
https://github.com/philpax/ggllama (also ggml based)
There's also a discussion on GitHub issue on setzer's repo to collaborate a bit on these separate efforts: https://github.com/setzer22/llama-rs/issues/4
- Rust+OpenCL+AVX2 implementation of LLaMA inference code
- Pure Rust CPU and OpenCL implementation of LLaMA language model
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?
llama.cpp - LLM inference in C/C++
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
alpaca-lora - Instruct-tune LLaMA on consumer hardware
ultraviolet - A wide linear algebra crate for games and graphics.
mlc-llm - Universal LLM Deployment Engine with ML Compilation
litestar - Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
80r3d
llm - An ecosystem of Rust libraries for working with large language models