Local CLI Copilot, powered by Ollama. 💻🦙
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Updated
Feb 22, 2026 - Go
Local CLI Copilot, powered by Ollama. 💻🦙
[EMNLP 2025] OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
AI inference engine for Java in Java. Fast.
Experimental tools to backdoor large language models by re-writing their system prompts at a raw parameter level. This allows you to potentially execute offline remote code execution without running any actual code on the victim's machine or thwart LLM-based fraud/moderation systems.
[CVPR 2026 Highlight] PersonaVLM: Long-Term Personalized Multimodal LLMs
PasLLM - LLM inference engine in Object Pascal (synced from my private work repository)
Deploy open-source LLMs on AWS in minutes — with OpenAI-compatible APIs and a powerful CLI/SDK toolkit.
A local inference LLM coding agent.
Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and other open source LLMs. Sentiment Analysis, Application Context based ChatBots. Custom Data Handling. LLMs - GPT 3.5 / 4o, Gemini Pro 1.5, Claude 3, Llama 3.1, Phi-3, Gemma 2, Falcon 3, Qwen 2.5, Mistral Nemo, Wizard Math
Inspired by 《Learning to Reason in 13 parameters》, use TinyLoRA+GRPO(32 parameters) to fine-tune Qwen2.5-Coder-3B-Instruct(or other models) to accomplish competitive programming.
Hand-derived memory-efficient VJPs for tuning LLMs on laptops.
Community-built Qwen AI Provider for Vercel AI SDK - Integrate Alibaba Cloud's Qwen models with Vercel's AI application framework
Local, portable GUI for Qwen3-TTS. Optimized for NVIDIA RTX 50 Series (CUDA 12.8). One-click install.
Exploring Agno framework for building AI agents.
Get Clothes from image
Simple RAG system powered by Milvus.
ADHARA is a prototype early warning system that identifies learning friction during normal learning activities. It supports educators by flagging behavioral indicators before academic failure occurs.
vLLM tool parser for Qwen2.5-Coder models using <tools> tag format.
grpo to train long form QA and instructions with long-form reward model
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