README.md

---
license: mit
base_model: deepseek-ai/DeepSeek-V4-Flash
tags:
  - gguf
  - llama.cpp
  - moe
  - expert-pruning
  - deepseek4
  - coding
library_name: llama.cpp
---

# ARustyCoder93

A 93-of-256 routed-expert cut of DeepSeek-V4-Flash in GGUF, made for local
agentic coding on one 96 GB GPU. **This is not a DeepSeek release.** It is a
derived model produced by expert pruning; DeepSeek did not make or endorse
it. It inherits DeepSeek-V4-Flash's MIT license.

Full recipe, scripts, llama.cpp patch and every checksum:
https://github.com/apchat-agent/arustycoder

| | |
|---|---|
| file | `arustycoder93.gguf` — 62,213,283,424 bytes |
| sha256 | `90a4c8a8a32fc664d822f1df093d5e228111c94eebf9398ae5c5737391ecb058` |
| source | `antirez/deepseek-v4-gguf` → `DeepSeek-V4-Flash-MXFP4Experts-F16HC-F16Compressor-F16Indexer-Q8Attn-Q8Shared-Q8Out-chat-v2-mxfp4-0731.gguf` (MXFP4 experts, Q8 attention/shared/output, F16 compressor/indexer); kept tensors are byte-identical to it |
| experts | 93 per layer kept out of 256 (top-6 routing unchanged); 43 MoE layers |
| runs on | stock llama.cpp at or after commit `22b8e31` (2026-08-15). Our serving build for the 2026-08-16 smoke tests was that commit plus the census patch from the recipe repo, which is inert for this file (no full-router tensors). `llama-server -m arustycoder93.gguf -ngl 99 -c 262144 -fa on` |
| footprint | ~67 GB resident at 256K context on an RTX PRO 6000; ~46 tok/s decode, ~470 tok/s prompt |

## How the 93 experts were chosen

Two "grow-from-nothing" censuses (start from a random 8 experts per layer
with the full 256-wide router kept as side tensors, record which experts the
router *wants*, rebuild at 16, 32, 64) — one on a code/systems corpus, one on
a general corpus. ARustyCoder93 is the **union** of the two keep-64 sets:
78..93 experts per layer (mean 87.3), padded to 93 in the smaller layers by
merged rank because a GGUF needs a single expert count. Each keep-64 alone
predicts held-out text well but cannot finish generating; the union does.
Method, numbers and the merge rule are in the repository README.

## What it does and does not do

- 6/6 coding asks (write Rust, fix Python, explain C, refactor JS, a tool
  call, a multi-file plan) finished cleanly, 86-1993 completion tokens, with
  the server settings above. Prompts and raw responses are in the recipe
  repo under `results/`.
- Known limit: a harder single-shot prompt (a generic LRU cache) fell into a
  verbatim repetition loop in its reasoning to a 12k-token cap. Expect this
  on some hard reasoning; a DRY sampler stops the loop but corrupts
  identifiers in code.
- No standard benchmark has been run on this cut. It is an experiment that
  turned out useful for interactive coding. Use the unpruned model when
  correctness matters more than footprint.

## Files

- `arustycoder93.gguf` — the model
- `config.txt`, `prune.log` — build record of this exact file
- `merged-wants.tsv`, `coder-census-64.wants.tsv`, `general-census-64.wants.tsv` — the expert rankings it was pruned from
- `PROVENANCE.md` — checksums, versions, commits
- `LICENSE-DeepSeek-V4-Flash` — the upstream MIT notice (DeepSeek); `LICENSE` — MIT for the recipe

HF metadata note: `base_model` points at DeepSeek-V4-Flash; there is no
"pruned" relation tag on the Hub, so none is set.

## Provenance / credit

Base model: DeepSeek-V4-Flash by DeepSeek (MIT). GGUF source and its
quantization: antirez/deepseek-v4-gguf (tagged MIT on the Hub). Pruning
method, patch and scripts: this project (MIT), 2026-08-15/17.