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"
| # 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). | |