December 2022 - cypherpunks - lists.cpunks.org

Chia is one of the most decentralized blockchains by node count
by professor rat 07 Dec '22

07 Dec '22

>>> with more than 100,000 global nodes, and we constructed our smart contracting language, based on Lisp, with security as the highest priority https://www.chia.net Reposts not product endorsements

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Cryptoanarchy has a severely underutilized spam-relay-filter
by professor rat 07 Dec '22

07 Dec '22

Its called APster and dates back to the mid-90's list - originally misdiagnosed as a bug! TIMOTHY " An anonymous computerized market will even make possible abhorrent markets for assassinations and extortion " C. MAY 1992 Post 1995 soon becoming a feature TIM " I guess now that the Pentagon is setting up a murder pool it can't be illegal for us to do it " MAY. 2003 https://marc.info/?l=cypherpunks Our registry may be a little corrupted but then again - what isn't?

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Kolektiva.social is a Mastodon instance built by and for anarchists
by professor rat 07 Dec '22

07 Dec '22

This is our chance to re-imagine social media, and our relationship to it. To rediscover the lost promise of the Internet. To construct new digital worlds in the burning shell of the old. And to do it together. The old paradigms are dying. It’s long past time that those who want to overthrow this miserable system break our addiction to data-mining surveillance platforms, built and maintained by our enemies. https://anarchistnews.org/content/kolektiva-social-media-and-anarchists Reposts not endorsements of failing aggregation portals

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Barry the Hunted
by professor rat 06 Dec '22

06 Dec '22

https://twitter.com/J0E007/status/1483717638220132355?cxt=HHwWhsC90ZbVnJcpA… Repsots from twats

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Cryptoanarchy jacking the culture
by professor rat 06 Dec '22

06 Dec '22

A societies culture may follow its political economy - or be led by it - or both - but either way its an indicator of our success when stuff like this happens. >>> A pair of popular electronic musicians who are also prominent NFT backers are teaming up—and drawing inspiration from their respective CryptoPunks profile pictures (PFPs). Steve Aoki and Justin “3LAU” Blau announced today that they have formed a supergroup via their Ethereum NFTs and will release new music under the name Punx. Though both are seasoned producers with their own respective sonic signatures, 3LAU tweeted that they plan to deliver a new style of electronic music through their Punx collaboration. https://decrypt.co/116571/djs-steve-aoki-3lau-form-cryptopunks-supergroup Reposts not Parisian's tiring of sans-culottes and red-caps

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The crypto-currency sector where being handcuffed to a corpse is a feature; not a bug
by professor rat 06 Dec '22

06 Dec '22

>>> the Federal Reserve, which has jacked up interest rates and plans to keep doing so. For Circle and other stablecoin issuers, this has amounted to a windfall since they’re sitting on billions of dollars in reserve funds that can be invested in T-bills to earn interest https://fortune.com/crypto/2022/12/06/the-crypto-industry-is-a-basket-case-… Reposts not necrophiliac ghouls called CIRCLE and TETHER

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Secretary Yellen, We’ve Got a “Staggering” Problem: New Report Shows Foreign Banks Have Secret Derivative Debt that Is “10 Times their Capital”
by professor rat 06 Dec '22

06 Dec '22

Speaking of staggering loads . . . Circle claims that each USDC is backed by a dollar held in reserve, or by other “approved investments”, though these are not detailed. The wording on the Circle website changed from the previous “backed by US dollars” to “backed by fully reserved assets” in June 2021. https://en.wikipedia.org/wiki/USD_Coin USDC reserves are regularly attested (but not audited) by Grant Thornton, LLP As of July 2022, Circle states that there are 55 billion USDC in circulation On October 15, 2021, Tether Limited paid a fine of USD $41.6 million for inaccurately claiming that minted USD₮ were 100% backed by fiat USD, when they were actually 100% backed by a combination of fiat USD and other assets such as “unsecured receivables, commercial papers, funds held by third parties, and other non-fiat assets” See your 55 - raise you 10! How many Tether USDT coins are there in circulation? USDT Price Live Data The current CoinMarketCap ranking is #3, with a live market cap of $65,504,587,968 USD. It has a circulating supply of 65,496,215,611 USDT coins and the max. supply is not available. Clap hands if you believe in these two crypto-banks and Fairies I think the whole FTX contretemps involves about 11 billion - not 55 - not 65. So whats the rumpus? We’re just warming up - billions is chump-change. WELCOME to the REVOLUTION! FLOOD the ZONE with FREE-MONEY!

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After 1.9 Million Votes, Warnock-Walker 2.0 Reaches Decision Day With Runoff
by Gunnar Larson 06 Dec '22

06 Dec '22

After 1.9 Million Votes, Warnock-Walker 2.0 Reaches Decision Day With Runoff https://www.bloomberg.com/news/articles/2022-12-06/georgia-voters-set-to-re…

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[ot] HF:BS made a distributed model trainer a bit ago
by Undescribed Horrific Abuse, One Victim & Survivor of Many 06 Dec '22

06 Dec '22

https://github.com/bigscience-workshop/petals Easy way to run 100B+ language models without high-end GPUs by joining compute resources with people across the Internet. 10x faster than offloading. Generate text using distributed BLOOM and fine-tune it for your own tasks: ```python from petals.client import DistributedBloomForCausalLM # Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloom-petals", tuning_mode="ptune") inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"] outputs = model.generate(inputs, max_new_tokens=5) print(tokenizer.decode(remote_outputs[0])) # A cat sat on a mat... # Training (updates only prompts or adapters hosted locally) optimizer = torch.optim.AdamW(model.parameters()) for input_ids, labels in data_loader: outputs = model.forward(input_ids) loss = cross_entropy(outputs.logits, labels) optimizer.zero_grad() loss.backward() optimizer.step() ``` 🚀  <a href="https://colab.research.google.com/drive/1Ervk6HPNS6AYVr3xVdQnY5a-TjjmLCdQ?u…">Try now in Colab</a> Connect your own GPU and increase Petals capacity: ```bash (conda) $ pip install git+https://github.com/bigscience-workshop/petals (conda) $ python -m petals.cli.run_server bigscience/bloom-petals ``` 💬 If you have any issues or feedback, please join [our Discord server](https://discord.gg/uGugx9zYvN)! Check out more tutorials: - Training a personified chatbot: [notebook](./examples/prompt-tuning-personachat.ipynb) - Fine-tuning BLOOM for text semantic classification: [notebook](./examples/prompt-tuning-sst2.ipynb) - Launching your own swarm: [tutorial](https://github.com/bigscience-workshop/petals/wiki/Launch-your-ow… - Running a custom foundation model: [tutorial](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-m… ## How it works? - **Petals** runs inference or fine-tunes large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom) by joining compute resources with people all over the Internet. - One participant with weak GPU can load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. - Inference runs at ≈ 1 sec per step (token) — 10x faster than possible with offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. - Beyond classic language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. This combines the comforts of an API with the flexibility of PyTorch. <img src="https://i.imgur.com/RTYF3yW.png" width="800"> 📜  <a href="https://arxiv.org/pdf/2209.01188.pdf">Read paper</a> ### 📋 Terms of use Before using Petals to run a language model, please make sure that you are familiar with its terms of use, risks, and limitations. In case of BLOOM, they are described in its [model card](https://huggingface.co/bigscience/bloom) and [license](https://huggingface.co/spaces/bigscience/license). ### 🔒 Privacy and security If you work with sensitive data, you should only use a private swarm (or a subset of servers in the public swarm) hosted by people and institutions you trust, who are authorized to process this data. This is important because it's technically possible for peers serving model layers to recover input data or model outputs. Also, if there are malicious peers, they may alter their outputs to influence the model outputs. See a more detailed discussion in Section 4 of our [paper](https://arxiv.org/pdf/2209.01188.pdf). ## FAQ 1. **What's the motivation for people to host model layers in the public swarm?** People who run inference and fine-tuning themselves get a certain speedup if they host a part of the model locally. Some may be also motivated to "give back" to the community helping them to run the model (similarly to how [BitTorrent](https://en.wikipedia.org/wiki/BitTorrent) users help others by sharing data they have already downloaded). Since it may be not enough for everyone, we are also working on introducing explicit __incentives__ ("bloom points") for people donating their GPU time to the public swarm. Once this system is ready, people who earned these points will be able to spend them on inference/fine-tuning with higher priority or increased security guarantees, or (maybe) exchange them for other rewards. 2. **Why is the platform named "Petals"?** "Petals" is a metaphor for people serving different parts of the model. Together, they host the entire language model — [BLOOM](https://huggingface.co/bigscience/bloom). While our platform focuses on BLOOM now, we aim to support more [foundation models](https://arxiv.org/abs/2108.07258) in future. ## Installation Here's how to install Petals with conda: ``` conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch pip install git+https://github.com/bigscience-workshop/petals ``` This script uses Anaconda to install cuda-enabled PyTorch. If you don't have anaconda, you can get it from [here](https://www.anaconda.com/products/distribution). If you don't want anaconda, you can install PyTorch [any other way](https://pytorch.org/get-started/locally/). If you want to run models with 8-bit weights, please install **PyTorch with CUDA 11** or newer for compatility with [bitsandbytes](https://github.com/timDettmers/bitsandbytes). __System requirements:__ Petals only supports Linux for now. If you don't have a Linux machine, consider running Petals in Docker (see our [image](https://hub.docker.com/r/learningathome/petals)) or, in case of Windows, in WSL2 ([read more](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-cuda-in-wsl)) CPU is enough to run a client, but you probably need a GPU to run a server efficiently. ## 🛠️ Development Petals uses pytest with a few plugins. To install them, run: ```python git clone https://github.com/bigscience-workshop/petals.git && cd petals pip install -e .[dev] ``` To run minimalistic tests, you need to make a local swarm with a small model and some servers. You may find more information about how local swarms work and how to run them in [this tutorial](https://github.com/bigscience-workshop/petals/wiki/Launch-your-ow…. ```bash export MODEL_NAME=bloom-testing/test-bloomd-560m-main python -m petals.cli.run_server $MODEL_NAME --block_indices 0:12 \ --identity tests/test.id --host_maddrs /ip4/127.0.0.1/tcp/31337 --new_swarm &> server1.log & sleep 5 # wait for the first server to initialize DHT python -m petals.cli.run_server $MODEL_NAME --block_indices 12:24 \ --initial_peers SEE_THE_OUTPUT_OF_THE_1ST_PEER &> server2.log & tail -f server1.log server2.log # view logs for both servers ``` Then launch pytest: ``` export MODEL_NAME=bloom-testing/test-bloomd-560m-main REF_NAME=bigscience/bloom-560m export INITIAL_PEERS=/ip4/127.0.0.1/tcp/31337/p2p/QmS9KwZptnVdB9FFV7uGgaTq4sEKBwcYeKZDfSpyKDUd1g PYTHONPATH=. pytest tests --durations=0 --durations-min=1.0 -v ``` After you're done, you can terminate the servers and ensure that no zombie processes are left with `pkill -f petals.cli.run_server && pkill -f p2p`. The automated tests use a more complex server configuration that can be found [here](https://github.com/bigscience-workshop/petals/blob/main/.github/workf…. ### Code style We use [black](https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html) and [isort](https://pycqa.github.io/isort/) for all pull requests. Before committing your code, simply run `black . && isort .` and you will be fine. -------------------------------------------------------------------------------- This project is a part of the <a href="https://bigscience.huggingface.co/">BigScience</a> research workshop. <img src="https://petals.ml/bigscience.png" width="150">

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who is grancrap - grarpamp@gmail.com
by punk 06 Dec '22

06 Dec '22

it's funny how agent grancrap, US nazi and jewmail user, hides behind his jewmail account while 'doxing' people here. it's hard to be a bigger, cowardly piece of shit and coward =) then again, we're talking about US jewnazis here.... Show some balls, turd, tell us your real name.

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