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 / PROVENANCE.md

ayourtch

Upload folder using huggingface_hub

fa8d379 verified 3 days ago

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Provenance — ARustyCoder93

Everything below was recorded on the build host at build time or checked against the files afterwards. Dates are UTC unless marked.

Output

file	bytes	sha256
`arustycoder93.gguf` (built as `deepseek4-keep93.gguf`, 2026-08-16 19:25 UTC)	62,213,283,424	`90a4c8a8a32fc664d822f1df093d5e228111c94eebf9398ae5c5737391ecb058`

GGUF header: arch deepseek4, expert_count = 93, expert_used_count = 6, 43 MoE layers (3 hash-routed + 40 router layers), all other tensors and metadata copied byte-for-byte from the source. Coarse ftype label as shown by llama.cpp is not a quant family — the file is mixed (MXFP4 experts, Q8/F16 elsewhere), exactly as in the source.

Source model

item	value
repo	https://huggingface.co/antirez/deepseek-v4-gguf (MIT), base `deepseek-ai/DeepSeek-V4-Flash` (MIT)
file	`DeepSeek-V4-Flash-MXFP4Experts-F16HC-F16Compressor-F16Indexer-Q8Attn-Q8Shared-Q8Out-chat-v2-mxfp4-0731.gguf`
bytes	155,976,458,848 (matches the HF tree listing)
sha256	`0e3a161b670f686128ec5f92a601dfde616a37bf5e7e48999fa2d32471b57ec6`
arch	deepseek4, 43 layers × 256 routed experts, top-6 + 1 shared; layers 0-2 hash-routed via `ffn_gate_tid2eid`

Build host

item	value
GPU	NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 96 GB (97887 MiB), power limit 350 W; one GPU used (`CUDA_VISIBLE_DEVICES=0`)
driver / CUDA	610.57.04 / CUDA 13.3 (nvcc V13.3.73)
OS	Ubuntu 24.04.4 LTS, kernel 6.8.0-137-generic
llama.cpp	`ggml-org/llama.cpp` commit `22b8e310b921d568e013e4533002be5a8fe53f17` (2026-08-15, "server: re-design yield_to_queue thread model (#27133)") + `llama.cpp-patch/wants-census.patch`; built Release, `GGML_CUDA=ON`, `GGML_NATIVE=ON`, gcc/g++ from Ubuntu 24.04
python	3.12.3 (venv), `gguf==0.19.0`, `numpy==2.5.2`, `tqdm==4.70.0`, `PyYAML==6.0.3`

Scripts (as run; identical to the copies in `scripts/`)

file	sha256
`prune_experts.py`	`f1ea4f50c49c354da32a64e232b47e2a8e41ec178483824a6ef1765ca0bf5962`
`compare_sets.py`	`0d6992654b3cd2a08c0bbba643e83c638583ff0c53c888dba2c0be6db40bb76c`
`apply_wants_patch.py`	`592f3f34a76a0786fbbb512af9120252cbab59d93359aca102b8c3e7916b4406`
`grow.sh`	`1d49a05c619be943552660312a1e5391cdb1664c9c3ba1e5964f10f88458ff3c`
`grow2.sh`	`259ed5e0c78a15b1b85f67dc9cf5dd3d79b48eba84fce2df7bc29df1912602f1`
`grow-coder.sh`	`9dd532ca77f56b5f1eb1c84dab4ec0f28839f6042a3fed8f737d36ed6c4b32fd`
`build_corpus.py`	`a516e863acf76136334e417dfe3590ef2d4fce3a8bb9ff73c5a17d9745b7b3c4`
`build_coder_corpus.py`	`36a762ae06ba5ae5a9f26327b5e74d0276b765d9ed0f95203d3895bf8cc8ec6b`
`merge_wants.py`	`62427369e07ccdb03afc7d0bce3d58c133f770b4275b91a884e52eead0158a4e` — written after the fact (2026-08-17) as the re-implementation of the inline merge; verified top-93-identical per layer against `merged-wants.tsv`
`grow-ppl-paired.sh`	`c2272bcc1edb5d1d427542cdc23fe1deb5268baf9df1edc2834952a54c97cafc`
`grow-ppl-ci.sh`	`e0350ea7d3490d3b98c626366e37f37f0acb29f6f5f2a3553e9f7e5eaafaf00f`
`ppl-paired.py`	`349be3a0af9f7c53e79d1bf620d919b396a65bdcf84f063eaa27046b5addee57` (the paired-test tool; results/RESULTS.md)
`code-smoke.py`	`cd2fb11c48f43fb02887ef717ece92ac632723ad4c128249c8164154ee093620`

grow-coder.sh as published differs from the as-run copy in two comment/ provenance lines (an internal repo path and a wiki reference were removed); the sha256 above is the as-run file. Likewise configs/grow-coder/config.txt had one appended correction paragraph shortened and the hostname redacted in all three config.txt; nothing numeric changed.

Census / ranking files (in `wants/`)

file	sha256	role
`coder-census-64.wants.tsv` (was `grow-coder/census-64.imatrix.wants.tsv`)	`bead82b2976d9c48ae4339c1f2ae2c1d32e72cc1386b530078258e572658cd0a`	final code-grow census, input A
`general-census-64.wants.tsv` (was `grow/census-64.imatrix.wants.tsv`)	`f20041fac879f2c4553f416fc03383d4bd30ea0435b38144668d97ffb665621b`	final general-grow census, input B
`merged-wants.tsv`	`8152442949c297b0dc159533f71f883165a709df5b3deaa4debf07b3b02b263b`	the ranking the model was pruned with (`PRUNE_WANTS`)

Final prune command (from configs/grow-union/config.txt):

PRUNE_WANTS=merged-wants.tsv python prune_experts.py SRC.gguf deepseek4-keep93.gguf 93 6

Corpora (not redistributed; rebuild with the builders + these sources)

file	bytes	sha256	built from
`coder-train.txt`	4,197,328	`f1698b26cc486134027b9207eaf0109a1b716e2c0643c74049a7681685466122`	`build_coder_corpus.py`: ripgrep `crates/*/.rs`, llama.cpp `src/ggml/tools` C/C++ + `gguf-py`/`scripts` Python + headers, Rust book `src/*.md`, OWASP CheatSheetSeries, Linux man pages; 4000-char chunks, `random.seed(7)`, every 10th chunk to heldout
`coder-heldout.txt`	468,010	`18314cd45eccf14cda9fee14542a6ee0b7bf9c9022bf27776bef2a6b5e13f7b0`	same
`domain-train.txt`	—	`a3e3fd1a326e6568f7ea944d186cc8826a198bdec0fcd14a92d1c2e1f1b6e045`	`build_corpus.py`: Rust book, ripgrep, OWASP, man pages, GSM8K (first 1800 rows), plus ~0.7 MB of our own model reasoning traces (`agentic.txt`, sha256 `ae4f8f386dd9fdff1cd72385561dfaae2eb84ee82d2b958c51c6d7a5b4723e05`, not published)
`domain-heldout.txt`	—	`78e379ccaa5b5dfcb0bbb2f106c78aa10940069e484d238afb499ec683f111aa`	same
`gsm8k.jsonl`	4,166,206	`17f347dc51477c50d4efb83959dbb7c56297aba886e5544ee2aaed3024813465`	GSM8K train split, one JSON object per line (`question`, `answer`)
`man.txt`	1,602,349	`a95d16db24d8ae616ef582c657c900f522fe77b26be6313fa0a644b3a740d213`	concatenated `man` output from the build host (starts with `SYSTEMD(1)`)
`ds4flash-domain-200.imatrix`	—	`26ceeec5a0f1c6f29809c4154c4eb1872f5518eddfd9125732dc3a354f4b701e`	full-model "truth" census over `domain-train.txt` (205 chunks); used only for comparison
`wiki.test.raw`	—	`173c87a53759e0201f33e0ccf978e510c2042d7f2cb78229d9a50d79b9e7dd08`	wikitext-2 test, PPL reference only

Corpus source checkouts on the build host:

repo	commit
github.com/BurntSushi/ripgrep	`3fce3b5bb0236da2df6d99672afb8a719642eca7`
github.com/OWASP/CheatSheetSeries	`07111ee754e832e335377ac64fd0f8f848d9029c`
github.com/rust-lang/book	`917544888a55e4da7109bdba8c88c893c0da70f4`
github.com/ggml-org/llama.cpp (code corpus source)	`22b8e310b921d568e013e4533002be5a8fe53f17` (working tree with the wants patch applied)

Grow runs

General grow (grow.sh, then grow2.sh after a pruner parser crash at the keep-16 stage; 2026-08-15 21:57 → 23:28 UTC per configs/grow-general/grow.log and file mtimes): random keep-8 (PRUNE_SEED=1, PRUNE_FULL_ROUTER=1) → census 100 chunks of domain-train.txt at -c 2048 -b 2048 -ub 2048 → keep-16 → keep-32 → keep-64; final grown-keep64.gguf (45,531,490,912 bytes, from ls; no sha256 recorded) built from census-64.imatrix.wants.tsv with a normal pruned router. Per-stage logs in configs/grow-general/. This run predates the config-record habit, so it has no config.txt; the driver and logs are the record. The keep-8 stage ran with the pre-fix pruner (the bug was in PRUNE_WANTS parsing, which the random keep-8 stage does not use); every later stage used the pruner at the sha256 above.

Code grow (grow-coder.sh, 2026-08-15 23:44 → 2026-08-16 01:16 UTC): same schedule on coder-train.txt; full record in configs/grow-coder/config.txt (final deepseek4-coder-keep64.gguf 45,531,490,912 bytes, sha256 d9630be3be692823db1460d544035dda737f30d72bb84c8da2c9d0071e5fb186).

Union build (2026-08-16 19:25 UTC): configs/grow-union/config.txt + prune.log. Bytes in 145.3 GiB → out 57.9 GiB (tensor payload); file 62,213,283,424 bytes.

merge-128 (configs/grow-merge/, 2026-08-16 07:43 UTC): the earlier union + ~40 filler experts at keep-128. Generated cleanly but did badly on an internal 92-question reasoning eval (most cases hit the token cap; of the 17 it finished within budget it got 9 right where the unpruned base got 16) and was deleted; kept here because it is where the +1e7 union-bonus rule was found and verified.

Serving (as run for the smoke tests)

llama-server -m arustycoder93.gguf -ngl 99 -c 262144 -fa on

~67 GB resident on the PRO 6000 at 256K context; ~46 tok/s decode, ~470 tok/s prompt processing (from the server log, single request).

Provenance — ARustyCoder93

Output

Source model

Build host

Scripts (as run; identical to the copies in scripts/)

Census / ranking files (in wants/)

Corpora (not redistributed; rebuild with the builders + these sources)

Grow runs

Serving (as run for the smoke tests)

Scripts (as run; identical to the copies in `scripts/`)

Census / ranking files (in `wants/`)