🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
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Updated
Aug 5, 2024 - Python
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Large Language Model Text Generation Inference
🩹Editing large language models within 10 seconds⚡
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.
Fast Inference Solutions for BLOOM
💬 Chatbot web app + HTTP and Websocket endpoints for LLM inference with the Petals client
Train llm (bloom, llama, baichuan2-7b, chatglm3-6b) with deepspeed pipeline mode. Faster than zero/zero++/fsdp.
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
LLMs4OL: Large Language Models for Ontology Learning
一套代码指令微调大模型
Generate README.md with GPT-3 few-shot learning
Finetuning BLOOM on a single GPU using gradient-accumulation
Scrapy Redis with Bloom Filter,support redis sentinel and cluster
Easy-to-use framework for evaluating cross-lingual consistency of factual knowledge (Supported LLaMA, BLOOM, mT5, RoBERTa, etc.) Paper here: https://aclanthology.org/2023.emnlp-main.658/
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