Instructions to use ayourtch/ARustyCoder93 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ayourtch/ARustyCoder93 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ayourtch/ARustyCoder93
# Run inference directly in the terminal:
llama cli -hf ayourtch/ARustyCoder93

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ayourtch/ARustyCoder93
# Run inference directly in the terminal:
llama cli -hf ayourtch/ARustyCoder93

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ayourtch/ARustyCoder93
# Run inference directly in the terminal:
./llama-cli -hf ayourtch/ARustyCoder93

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ayourtch/ARustyCoder93
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ayourtch/ARustyCoder93

Use Docker

docker model run hf.co/ayourtch/ARustyCoder93

LM Studio
Jan
Ollama
How to use ayourtch/ARustyCoder93 with Ollama:
```
ollama run hf.co/ayourtch/ARustyCoder93
```

Unsloth Studio

How to use ayourtch/ARustyCoder93 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ayourtch/ARustyCoder93 to start chatting

How to use ayourtch/ARustyCoder93 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ayourtch/ARustyCoder93"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ayourtch/ARustyCoder93 with Docker Model Runner:
```
docker model run hf.co/ayourtch/ARustyCoder93
```

Lemonade

How to use ayourtch/ARustyCoder93 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ayourtch/ARustyCoder93

Run and chat with the model

lemonade run user.ARustyCoder93-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ayourtch/ARustyCoder93 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ayourtch/ARustyCoder93

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ayourtch/ARustyCoder93 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ayourtch/ARustyCoder93

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ayourtch/ARustyCoder93" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ARustyCoder93 / README.md

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metadata

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.