sentencepiece
v-diffusion-pytorch
sentencepiece | v-diffusion-pytorch | |
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19 | 10 | |
9,722 | 704 | |
2.5% | - | |
8.2 | 0.0 | |
10 days ago | over 1 year ago | |
C++ | Python | |
Apache License 2.0 | MIT License |
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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
v-diffusion-pytorch
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Leaked deck raises questions over Stability AI’s Series A pitch to investors
This is dumb.
We employed Eleuther team members as Stability AI employees/contractors and incubated them until the 501(c)3 was set up and we managed to bring in other funders too: https://techcrunch.com/2023/03/02/stability-ai-hugging-face-...
I am on the board and delighted to continue to support their work as an independent organisation for LM evaluation, alignment and interpretability which is much needed.
Indeed though our approach was handing out significant compute for no control, no equity, no IP.
Anyone who has received Stability AI grants will be able to attest to this with multiple breakthroughs as a result, for example funding https://github.com/BlinkDL/RWKV-LM, the work of https://github.com/lucidrains and others.
Similarly we funded the beta of MidJourney with a cash grant for compute without ever even floating asking for equity etc as it is a market-creating innovation.
At the time MidJourney was using cc12m_1, a model developed by one of our lead (employed) generative AI developers Katherine Crownson / RiversHaveWings (https://github.com/crowsonkb/v-diffusion-pytorch)
Our model is simply to take open innovation and create commercial variants of that (our stable series models) from scratch and on our own, plus variants of that for private data - https://twitter.com/EMostaque/status/1649152422634221593?s=2...
This means we can be hands off versus other funders and trust researchers and help them succeed, something others do not.
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[D] Is Midjourney AI more-or-less the same architecture as DALL-E 2? Can I read about the model in detail somewhere or is there anything published in this regard?
From what I've gathered by being involved early in the beta / in other discords, Midjourney was originally based on a fine-tuned version of classifier-free guided v-diffusion. The fine-tuning dataset was a manually curated set largely from LAION-2B similar to the laion-art / laion-hd. To make it so fast they were using Progressive Distillation (possibly distilling on PLMS steps rather than p/ddim?) and settings optimized to let them skip a few of the first steps like Quick CLIP-Guided Diffusion. There's a good chance they were doing some prompt augmentation as well, although I think this would be susceptible to prompt discovery attacks which I haven't seen any examples of for Midjourney.
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Tweet: "Give us a few weeks, open version in the works." regarding an open Google Imagen-like system
Source. This tweet is from a person whose organization has been publicly credited with providing compute for others in the past (example: "Thank you to stability.ai for compute to train these models!").
- Does anyone know which GAN this is?
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Dall-E 2
com/RiversHaveWings/status/1462859669454536711, 2021.
[8] Katherine Crowson. v-diffusion. https://github.com/crowsonkb/v-diffusion-pytorch, 2021.
- How do I start creating my own AI generated art?
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Advice on improving Text to Image Model (CC12M Diffusion) model at higher output dimensions?
More parameters are available as seen in this code. The fix was adapted from this.
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Colab notebook "Text to Image (CC12M Diffusion)" from RiversHaveWings was updated with significantly faster image generation speed. It generates 4 images in 4.75 minutes (not including setup time) on a Tesla K80 GPU (free-tier Colab).
I'm not sure if this Colab notebook was mentioned in this sub previously, but it's been available since January 2022. The cc12m_1_cfg model used by this Colab notebook is different than the cc12m_1 model from this December 2021 post (reference).
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Airport carpets (a genurary submission)
A few more here https://twitter.com/metasemantic/status/1486334535436488705. Samples careful constructed with a heavily modified diffusion model by @rivershavewings https://github.com/crowsonkb/v-diffusion-pytorch
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Steampunk Airships
Most of the code was from Katherine Crowson's (@RiversHaveWings) v-diffusion-pytorch library (https://github.com/crowsonkb/v-diffusion-pytorch), which is an implementation of denoising diffusion probabilistic models (https://arxiv.org/abs/2006.11239). I used the CC12M_1 CFG checkpoint.
What are some alternatives?
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
dalle-mini - DALL·E Mini - Generate images from a text prompt
CTranslate2 - Fast inference engine for Transformer models
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
llama - Inference code for Llama models
gpt-3 - GPT-3: Language Models are Few-Shot Learners
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
dalle-2-preview
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
glide-text2im - GLIDE: a diffusion-based text-conditional image synthesis model
OpenNMT-Tutorial - Neural Machine Translation (NMT) tutorial. Data preprocessing, model training, evaluation, and deployment.
jaxtorch - A JAX nn library