LLMTools is a user-friendly library for running and finetuning LLMs in low-resource settings. Features include: 🔨 LLM finetuning in 2-bit, 3-bit, 4-bit precision using the ModuLoRA algorithm 🐍 Easy-to-use Python API for quantization, inference, and finetuning 🤖 Modular supp. Use it to navigate the topic and choose relevant methods, papers or tools.
This is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions.
An open-source visual environment for battle-testing prompts to LLMs. ChainForge is a data flow prompt engineering environment for analyzing and evaluating LLM responses.
This repo supports the paper "QLoRA: Efficient Finetuning of Quantized LLMs", an effort to democratize access to LLM research. QLoRA uses bitsandbytes for quantization and is integrated with Hugging Face's PEFT and transformers libraries.
This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu LongLLMLingua mitigates the 'lost in the middle' issue in LLMs, enhancing long-context information processing.
English | 简体中文 | 繁體中文 | Español | Français | Bahasa Indonesia | 日本語 | 한국어 | Русский | Tiếng Việt Discord · X · WeChat / Feishu 🐈 nanobot is an ultra-lightweight, open-source, self-hosted personal AI agent framework written in Python. It runs in a WebUI, terminal, or chat apps and combines tools, long-term memory, MCP integrations, model routing, multi-agent delegation, scheduled automation, and an OpenAI-compatible API in a small, readable core.
An OpenClaw-native workflow shell: typed (JSON-first) pipelines, jobs, and approval gates. OpenClaw (or any other AI agent) can use lobster as a workflow engine and avoid re-planning every step — saving tokens while improving determinism and resumability.
LLMTools is a user-friendly library for running and finetuning LLMs in low-resource settings. Features include: 🔨 LLM finetuning in 2-bit, 3-bit, 4-bit precision using the ModuLoRA algorithm 🐍 Easy-to-use Python API for quantization, inference, and finetuning 🤖 Modular supp. Use it to navigate the topic and choose relevant methods, papers or tools.
This is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions.
An open-source visual environment for battle-testing prompts to LLMs. ChainForge is a data flow prompt engineering environment for analyzing and evaluating LLM responses.
This repo supports the paper "QLoRA: Efficient Finetuning of Quantized LLMs", an effort to democratize access to LLM research. QLoRA uses bitsandbytes for quantization and is integrated with Hugging Face's PEFT and transformers libraries.
This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu LongLLMLingua mitigates the 'lost in the middle' issue in LLMs, enhancing long-context information processing.
English | 简体中文 | 繁體中文 | Español | Français | Bahasa Indonesia | 日本語 | 한국어 | Русский | Tiếng Việt Discord · X · WeChat / Feishu 🐈 nanobot is an ultra-lightweight, open-source, self-hosted personal AI agent framework written in Python. It runs in a WebUI, terminal, or chat apps and combines tools, long-term memory, MCP integrations, model routing, multi-agent delegation, scheduled automation, and an OpenAI-compatible API in a small, readable core.
An OpenClaw-native workflow shell: typed (JSON-first) pipelines, jobs, and approval gates. OpenClaw (or any other AI agent) can use lobster as a workflow engine and avoid re-planning every step — saving tokens while improving determinism and resumability.