Language Models

A research framework that assigns LLMs to trading-floor roles—analyst, researcher, trader, risk manager—to debate and execute simulated stock decisions.

It turns your document pile into a persistent, interlinked wiki so the LLM doesn't have to re-read everything every time you ask a question.

WeKnora exists to turn scattered enterprise documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining wiki.

SGLang exists to push low-latency, high-throughput inference for LLMs and multimodal models from a single GPU up to massive clusters.

It exists so you can compile and deploy large language models to phones, browsers, and nearly any consumer GPU from a single stack.

To give developers a single, general-purpose speech model that handles transcription, translation, and language identification by treating tasks as tokens to predict.

It centralizes model definitions so the same architecture works across PyTorch, JAX, vLLM, and llama.cpp without rewrites.

LocalAI wraps 36+ inference engines behind one OpenAI-compatible API and pulls them on demand, so you can run LLMs, vision, voice, and video on anything from a CPU to a Jetson.

It teaches how LLMs work by implementing tokenization, attention, pretraining, and finetuning in pure PyTorch, one notebook at a time.

The familiar high-level API now runs on three major backends, letting you swap execution engines without rewriting model code.

A hosted proxy that offers free, rate-limited API access to GPT, DeepSeek, and others for Chinese users who'd rather not tunnel through a VPN.

A systematic Chinese tutorial for developers who want to stop treating LLMs as black boxes and hand-build a 215-million-parameter model from the ground up.

Curated tutorials and tool reviews covering vibe coding, DeepSeek, Cursor, and the rest of the generative-AI menagerie, maintained as a free, open-source knowledge base.

A maintainer cataloged every Chinese NLP repo they touched into a single, obsessively categorized list so others wouldn’t have to hunt.

Official Python notebooks and guides for common OpenAI API tasks.

It exists so you can ask a database questions in English and let an agent write the SQL, run the Python, and generate the report without touching production unsupervised.

It exists so you can download, run, and chat with open-weight LLMs locally through one CLI and REST API, keeping inference on your own silicon.

LangExtract exists because asking an LLM to pull names and dates out of a report is easy; proving exactly which sentence each came from is the hard part.

A curated directory of software, plugins, and frameworks that integrate with the DeepSeek API, maintained by DeepSeek itself.

Because training a transformer shouldn't require 245MB of PyTorch just to multiply matrices.