A very simple framework for state-of-the-art Natural Language Processing (NLP)
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Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
- kv-cache
- llm-inference
- local-llm
- quantization
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
- calibration
- decision-model
- jev
- jev-model
TranslatedAI Cross-Border E-commerce Operations Agent Toolkit · Breaks operations SOPs down into 7 reusable AI Agent workflows, including prompts, templates, and real-world cases
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
Investor’s Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?
ReAlign models instantly, on demand. Per-request alignment for verified users and channels, or Always On. Use directly or with Hermes, OpenClaw and custom harnesses.
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
- ai4science
- llm
- multiagent-systems
- prompt-engineering
MCP server that connects AI agents to your running Neovim instance via msgpack-RPC — no plugins required.
Autonomous multi-agent framework to optimize scientific software
- acceleration
- agentic-ai
- ai-agents
- bioinformatics
Enterprise-grade platform to generate and execute Cypress, Playwright, WebdriverIO, and Appium end-to-end tests from natural language requirements. (Appium is experimental and requires external mobile infrastructure.)
Claude Code stock analysis skill: SEC EDGAR + market data, /analyze /score /compare — free, local Python tools. By XVARY Research.
- claude-code
- stock-analysis
- ai-skills
- anthropic