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Petals – Run LLMs at home, BitTorrent-style

▲ 33 points 12 comments by snorbleck 1h ago HN discussion ↗

Pangram verdict · v3.3

We believe that this document is fully human-written

1 %

AI likelihood · overall

Human
100% human-written 0% AI-generated
SEGMENTS · HUMAN 1 of 1
SEGMENTS · AI 0 of 1
WORD COUNT 166
PEAK AI % 1% · §1
Analyzed
Jul 23
backend: pangram/v3.3
Segments scanned
1 windows
avg 166 words each
Distribution
100 / 0%
human / AI fraction
Verdict
Human
Pangram v3.3

Article text · 166 words · 1 segments analyzed

Human AI-generated
§1 Human · 1%

Run large language models at home, BitTorrent‑style Generate text with Llama 3.1 (up to 405B), Mixtral (8x22B), Falcon (40B+) or BLOOM (176B) and fine‑tune them for your tasks — using a consumer-grade GPU or Google Colab. You load a part of the model, then join a network of people serving its other parts. Single‑batch inference runs at up to 6 tokens/sec for Llama 2 (70B) and up to 4 tokens/sec for Falcon (180B) — enough for chatbots and interactive apps. Beyond classic LLM APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch and 🤗 Transformers.

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This project is a part of the BigScience research workshop.