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

AirLLM slices giant transformers into layer shards so they fit in consumer VRAM without quantization or distillation.

DwarfStar bets that DeepSeek V4 Flash deserves its own self-contained C engine, complete with disk-persistent KV cache and asymmetrical 2-bit quantization, instead of yet another generic GGUF runner.

Kronos recasts noisy, multi-dimensional candlestick data as hierarchical discrete tokens so an autoregressive Transformer can forecast financial markets like a language model.

It exists to run large language models on virtually any hardware—from Apple Silicon to RISC-V to your browser—with zero external dependencies and minimal setup.

Meetily transcribes and summarizes meetings entirely on-device, because "we don't log your calls" is a promise best kept by physics.

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

Because swapping from GPT-4o to Claude shouldn't require rewriting your request plumbing.

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.

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

Official Jupyter notebooks demonstrating how to wire Claude into production tasks like RAG, SQL queries, and multimodal pipelines.

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.

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

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 Python framework for building production multi-agent systems that leans on LLM reasoning instead of rigid prompt choreography.

MiniMind is an educational training ground that rebuilds every stage of a modern language model—from tokenizer to RLHF—in raw PyTorch so you can see the gears turning instead of just calling high-level APIs.

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.

It exists because keeping up with the training loops, quantization tricks, and inference stacks of 100+ models is a full-time job most developers would rather delegate.

It automates the removal of transformer safety alignment so you don't have to hand-tune abliteration parameters or pay for expensive post-training.