alpaca.cpp
rllama
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| alpaca.cpp | rllama | |
|---|---|---|
| 94 | 7 | |
| 9,878 | 514 | |
| - | - | |
| 9.4 | 6.2 | |
| about 1 year ago | 2 months ago | |
| C | Rust | |
| MIT License | GNU Affero General Public License v3.0 |
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 -
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.
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
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
ultraviolet - A wide linear algebra crate for games and graphics.
ggml - Tensor library for machine learning
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
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.