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The Kimi K3 and DeepSeek-V4-Flash-0731 architectures, reimplemented from scratch in pure Rust (zero dependencies) and verified 1:1 against the official code.

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# microkimi

A zero-dependency Rust engine for two frontier MoE architectures - **Kimi K3** and **DeepSeek-V4-Flash-0731** - verified 1:1 against the official reference code.

**This is a framework for developers, including an end-to-end engine and a test model - not a model for end-users.**

> _Almost all of the code was written by Kimi K3 itself - the quiet beauty of an LLM giving birth to another._

> _The moon loved the sun, but they could never meet,_
> _so every night, they would go to the park to play._
> _Every night, the moon danced with the stars,_
> _and they were very happy, playing together every day._
> _The end._
>
> - nanokimi-0.2b, running on microkimi

## Quickstart

You need nothing but a Rust toolchain (`rustup`). The GitHub Releases ship only the trained **nano** models (`nanokimi-0.2b.bin` + `vocab_nano.json`) - the debug models are generated locally with `microkimi build` (they are architecture demos, not shipped).

```bash
cargo build --release
# download nanokimi-0.2b.bin + vocab_nano.json from the GitHub Releases page
# into the repo root, then:
./target/release/microkimi run "Once upon a time, a kind dragon lived in a cave. Every morning, he" \
  --model nanokimi-0.2b.bin --max-new 12 --raw
# answer:  would go to the park and play with his friends.

./target/release/microkimi chat --model nanokimi-0.2b.bin --raw      # interactive stories
./target/release/microkimi run "One day, a cat found a ball." \
  --model nanokimi-0.2b.bin --max-new 10 --raw --debug-routing       # watch the MoE router pick experts
```

## Models

Naming rule: **nano** models are trained from scratch here; **micro** models are pruned from the real weights; **-debug** files are synthetic fixtures for parity tests and engine tracing.

| model | what it is | how to get it |
|---|---|---|
| **nanokimi-0.2b** | 0.2B Kimi K3 model, trained from scratch | [Releases](https://github.com/microkimi/microkimi/releases) - see [KIMI.md](KIMI.md) |
| **microkimi-debug** | full 93-layer K3 skeleton, synthetic weights | `microkimi build` - see [KIMI.md](KIMI.md) |
| **microdeepseek-debug** | DeepSeek-V4 skeleton, synthetic weights | `microkimi build --arch dsv4` - see [DEEPSEEK.md](DEEPSEEK.md) |

## Commands

| task | Kimi K3 | DeepSeek-V4-Flash-0731 |
|---|---|---|
| assemble weights | `microkimi build` | `microkimi build --arch dsv4` |
| verify 1:1 vs official code | `microkimi paritytest` | `microkimi parity --arch dsv4` |
| all mechanism self-tests | `microkimi selftest` (covers both) | `microkimi selftest` (covers both) |
| generate | `microkimi run "..." --model microkimi-debug.bin` | `microkimi run "..." --model microdeepseek-debug.bin` |
| interactive | `microkimi chat --model nanokimi-0.2b.bin --raw` | `microkimi chat --model microdeepseek-debug.bin` |

`build-ds` and `dsparity` remain as aliases of `build --arch dsv4` / `parity --arch dsv4`.

## Engine features (both architectures)

| feature | what it does (measured, not promised) |
|---|---|
| MoE expert streaming | `--stream` keeps expert blobs on disk and fetches on demand (LRU + rollover in RAM), offset-sorted reads, direct I/O auto-detected: O_DIRECT on Linux, F_NOCACHE on macOS (`MICROKIMI_NO_ODIRECT=1` to A/B) |
| Markov prefetch | `--stream-predict N` pre-fetches the experts the router is likely to pick next; `microkimi cachereplay <trace>` replays a recorded request trace offline under LRU / Belady / Markov policies (record with `MICROKIMI_TRACE=trace.bin`) |
| mmap demand-paging | models are mapped, not loaded: the kernel pages weights on demand, so a model larger than RAM still runs (`MICROKIMI_NO_MMAP=1` for the old full-load path) |
| microquant | `microkimi slice --cold-vq N` keeps all experts but requantizes the coldest to 0.5-bit VQ - measured better than deleting them (30.6% vs 19.1% top-1 parity with the full model) |
| structural slicing | `microkimi slice` prunes layers / hidden channels / experts (`--layers --hidden --experts`) and vocabulary (`--vocab-top`) from a .bin or straight from remote safetensors; crash-safe resume (`.sliceckpt`) and a persistent expert-score cache |
| evaluation | `microkimi eval --model X.bin` - deterministic scorecard: 40 factual QA probes (2 phrasings) + perplexity, `--json` for archiving |
| memory packs | K3 only: `microkimi absorb doc.txt --out pack.mkmem` snapshots the fixed-size KDA state; `run --memory pack.mkmem` resumes it. A video-game save state - details in [KIMI.md](KIMI.md#memory-packs-save-states-for-a-neural-network) |

## License

MIT - see [LICENSE](LICENSE). Credits in [ACKNOWLEDGMENTS.md](ACKNOWLEDGMENTS.md).

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