llama-dl
sentencepiece
llama-dl | sentencepiece | |
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17 | 19 | |
3,386 | 9,520 | |
- | 2.1% | |
8.8 | 8.1 | |
about 1 year ago | 4 days ago | |
Shell | C++ | |
GNU General Public License v3.0 only | Apache License 2.0 |
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llama-dl
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Gitlab confirms it's removed Suyu, a fork of Nintendo Switch emulator Yuzu
There seems to be some confusion here. Let me step in as someone who has gone through this.
My repo https://github.com/shawwn/llama-dl was taken down last March by Facebook. They asserted copyright over LLaMA, which is obviously bogus since it was trained on data they do not own the copyright to. I was bummed about this, but after I mentioned on HN that I was willing to fight Meta, an anonymous person named L contacted me and sent $20k of Monero to cover legal fees. I was also contacted by an amazing lawyer who wanted to represent me in this. I was absurdly fortunate on both counts.
He drafted a counternotice, we sent it, and then my repo was restored within a week or so.
GitHub had no choice in the matter. Legally this is a required process. Ditto for GitLab. Both are US companies.
When YouTube-dl was taken down some time ago by a DMCA, Nat went to bat and got it restored, and GitHub made some sort of pledge to cover legal fees associated with bogus takedown requests.
Here’s the shitty part for this particular situation. A case can be made that the emulator is for the purpose of circumventing copyright protection mechanisms. This, sadly, is a solid legal basis for issuing a lawful takedown, as much as we all absolutely despise that idea. It’s pretty clear cut; Nintendo doesn’t want Switch games to be run on non-Nintendo platforms, and the emulator seeks to enable Switch games to be run on any platform. Therefore, the intent of the emulator is to circumvent Nintendo’s protection mechanisms.
So where does this leave us? Well, the team can file a counternotice. GitLab will restore the repo. But that opens up the team to a lawsuit by Nintendo. And as much as I want to stand up to bullies, there’s a difference between standing up to a guy shoving a kid in a locker vs standing up to a Silverback gorilla charging at you. Nintendo’s legal history implies the latter.
Welcome to Nintendo pain. The Smash community has been dealing with Nintendo’s BS for decades now. They shut down tournaments that use emulators for Smash Melee. And no one can do anything, because it’s their legal right to do so.
- [Chat Gpt] Metas LLaMA LLM ist durchgesickert – Führen Sie unzensierte KI auf Ihrem Heim-PC aus!
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Run LLaMA and Alpaca on your computer
Your philosophical argument is interesting, but what the op was saying was one of the linked repos in inaccessible due to DMCA: https://github.com/shawwn/llama-dl
So while what you say may be true the DMCA seems to have worth for these orgs because they can get code removed by the host, who is uninterested in litigating, and the repo owner likely is even less capable of litigating the DMCA.
Unfortunately as a tool of fear and legal gridlock DMCA has shown itself to be very useful to those with ill intent.
- Meta DMCAs llama-dl Repository
- Load LLaMA Models Instantly
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Is there some sort of open-source equivalent of this?
Here are some useful links: https://github.com/shawwn/llama-dl and https://rentry.org/llama-tard-v2#tips-and-tricks
- FLiP Stack Weekly for 13 March 2023
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Using LLaMA with M1 Mac and Python 3.11
Sure. You can get models with magnet link from here https://github.com/shawwn/llama-dl/
To get running, just follow these steps https://github.com/ggerganov/llama.cpp/#usage
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New JailBreak prompt + How to stop flagging/blocking!
https://rentry.org/llama-tard-v2#tips-and-tricks https://github.com/shawwn/llama-dl
- LLaMA, o ChatGPT da Meta vaza na internet e já pode ser baixada
sentencepiece
- sentencepiece
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LLM.int8(): 8-Bit Matrix Multiplication for Transformers at Scale
you need to train the model on 1 trillion tokens (https://platform.openai.com/tokenizer https://github.com/google/sentencepiece) anyways for it to get reasoning capacities, which it feels very unlikely that your data is that much.
I'm highly skeptical that you have enough data to pretrain if you don't have enough data to fine tune.
fine tuning + vector search + prompting of as much stuff as you can, on a LLM like palm2 or gpt4 is what I would do. otherwise you can use falcon 40B ofc.
maybe I should charge for this ahah
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[P] TokenMonster Ungreedy ~ 35% faster inference and 35% increased context-length for large language models (compared to tiktoken). Benchmarks included.
a) Comparison with SentencePiece tokenizer with comparable settings (It can also ignore word-boundaries and create phrase tokens)
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LLaMA tokenizer: is a JavaScript implementation available anywhere?
LLaMA uses the sentencepiece tokenizer: https://github.com/google/sentencepiece
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[P] New tokenization method improves LLM performance & context-length by 25%+
Besides, are you familiar with SentencePiece? What you are doing looks very similar (generate a large vocab, prune worst token until vocab size is reached), only the token selection criterion is different. It's also purely data driven in the sense that there are no assumption specific to language (and it can optionally segment across whitespace, as you are doing).
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Code runs without definition of function (automatically calls a different function instead)
Hi, I'm studying the implementation of encode and decode functions for Google's SentencePiece tokenizer.
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How to handle multiple languages in a sentence?
I think many LMs nowadays use unicode tokenizers, that are not tied to specific languages. E.g. sentencepiece is the most popular one: https://github.com/google/sentencepiece
- Large language models are having their Stable Diffusion moment
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LLaMA-7B in Pure C++ with full Apple Silicon support
If you are interested in implementing LLaMA yourself or learning, I noticed that the reference code by Facebook is one of the cleaner, easier to read ML code I've seen in a while. https://github.com/facebookresearch/llama/blob/main/llama/mo... It's about 200 lines long. You probably do need a bit of knowledge to understand what you are reading but I was pleasantly surprised.
For example in comparison, StableDiffusion torch code in diffusers and transformers Python libraries has lots of conditionals, experiments etc. that are not being used that can make it hard to follow what is going on.
Last weekend I got the "main loop" of the transformer working in pure CPU Rust code, following the reference code. My crappy code is just very very slow as I focused on getting it to run, not making it fast. The tokenizer uses some Google thing https://github.com/google/sentencepiece but luckily for inference it seems that you just need to be able to parse the tokenizer model file and not understand how it was created; I was able to strip out the protobuf files from that repository and add it to Rust and read the tokens.
I am optimistic that someone makes a high quality CPU or some CPU+GPU+SSD combination thingmaling that will make it somewhat practical to run even the large LLM models without needing an A100 or two.
- ChatGPT in an iOS Shortcut – Worlds Smartest HomeKit Voice Assistant
What are some alternatives?
llama.cpp - LLM inference in C/C++
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
llama - Inference code for Llama models
CTranslate2 - Fast inference engine for Transformer models
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
dalai - The simplest way to run LLaMA on your local machine
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
OpenNMT-Tutorial - Neural Machine Translation (NMT) tutorial. Data preprocessing, model training, evaluation, and deployment.