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
- llama.cpp
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
- Pi
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"
File size: 8,608 Bytes
fa8d379 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | # 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).
|