AI-powered Quantitative Investment Research Platform.
🔧 Fine-tune large language models efficiently on NVIDIA DGX Spark with LoRA adapters and optimized quantization for high performance.
🤖 Optimize your futures trading with LLM-TradeBot, an intelligent multi-agent system leveraging adversarial strategies for high win rates and low drawdowns.
🚀 Run modern 7B LLMs on legacy 4GB GPUs without crashes, breaking the VRAM barrier for developers facing GPU limitations.
🤖 Supercharge your AI development with a comprehensive template featuring 112 droids, custom commands, skills, and MCP integrations.
🤖 Explore and utilize top open-source tools for running, fine-tuning, and building LLMs entirely locally, without cloud dependencies or API keys.
🛠 Build and customize LLaMA models easily with LLaMA-Factory, streamlining the training and deployment of large language models.
🤖 Serve pre-trained AI models for real-time NLP tasks like sentiment analysis and entity recognition with a lightweight Flask API.
🤖 Build AI agents that combine OpenAI's orchestration and Claude's execution for effective production solutions.
📊 Transform documents into a smart knowledge base using Neo4j and Azure AI for efficient, intelligent searching and answer generation.
🌐 Run GGUF models directly in your web browser using JavaScript and WebAssembly for a seamless and flexible AI experience.
🚀 Simplify running, sharing, and shipping Hugging Face models with autopack; it quantizes and exports to multiple formats effortlessly.
🔍 Optimize RAG systems by exploring Lexical, Semantic, and Hybrid Search methods for better context retrieval and improved LLM responses.
Open-source quant finance foundation unites trading tools and protocols, funds community projects, and boosts cross-project interoperability for collaboration 🐙
Quantize TinyLlama-1.1B-Chat from PyTorch to CoreML (float16, int8, int4) for efficient on-device inference on iOS 18+.
Wrapture lets you go from a Python-trained model to deployable JavaScript with a single command. It generates TypeScript bindings and a Web/Node-compatible wrapper, using WebGPU/WASM-ready ONNX runtimes.
On-device LLM Inference Powered by X-Bit Quantization
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
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