TensorFold serves language models from a Zig binary on Apple Silicon and NVIDIA GPUs. The engine reads checkpoints, tokenizes requests and runs Metal or CUDA kernels directly. Serving needs no Python or MLX installation.
Building with a coding agent? Have it load the contributor skill before it writes any code.
Start with Qwen3.5-2B, the small 4-bit checkpoint checked on Metal and a GB10. The commands below download its
pinned revision, then serve it as local-model. Model weights have their own licenses.
Install with Homebrew, then start the server:
brew install ashhart/tensorfold/tensorfold
tensorfold pull mlx-community/Qwen3.5-2B-MLX-4bit@93760be4f1f69842a46bc13dbdc0f19e291392a3
tensorfold serve mlx-community/Qwen3.5-2B-MLX-4bit --name local-model --context 8192 --no-thinkingUse the release archive for your CPU: linux-aarch64 on a GB10, linux-x86_64 on an x86_64 host.
It needs glibc 2.28 or newer and an NVIDIA driver providing libcuda.so.1. Keep the archive's directories together.
See the CUDA runbook for runtime libraries and supported GPUs.
V=1.1.0
case "$(uname -m)" in
aarch64|arm64) PLATFORM=linux-aarch64 ;;
x86_64) PLATFORM=linux-x86_64 ;;
*) echo 'No release archive for this CPU' >&2; exit 1 ;;
esac
curl -fLO "https://github.com/ashhart/TensorFold/releases/download/v$V/tensorfold-$V-$PLATFORM.tar.gz"
curl -fLO "https://github.com/ashhart/TensorFold/releases/download/v$V/tensorfold-$V-$PLATFORM.tar.gz.sha256"
sha256sum -c "tensorfold-$V-$PLATFORM.tar.gz.sha256"
tar -xzf "tensorfold-$V-$PLATFORM.tar.gz"
export PATH="$PWD/tensorfold-$V-$PLATFORM/bin:$PATH"
tensorfold() { command tensorfold-native "$@"; }
tensorfold pull mlx-community/Qwen3.5-2B-MLX-4bit@93760be4f1f69842a46bc13dbdc0f19e291392a3
tensorfold serve mlx-community/Qwen3.5-2B-MLX-4bit --name local-model --context 8192 --no-thinkingLeave the server running. In another terminal, on either platform:
curl -fsS http://127.0.0.1:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"local-model","messages":[{"role":"user","content":"Say hello in one sentence."}],"max_tokens":128,"temperature":0}'The API also serves completions, Responses and Anthropic Messages. /v1/models lists the served name.
On Windows with an NVIDIA GPU, follow the native Windows build guide.
Use tensorfold --help, tensorfold capabilities --json and tensorfold serve REPO --help to inspect your binary.
You can also serve a complete local checkpoint directory or an existing Hugging Face cache.
The TensorFold desktop workspace bundles the local engine, model downloads, hardware recommendations, live generation metrics, coding workers and document editing.
Download the Apple-silicon beta 8 DMG, or build from app/.
Mobile clients in app/mobile/ remain preview software.
Living Weights teaches a running model new facts in its own weights. Start a Nemotron 3.5 Lightning server with
--slide and tell it something: each fact that passes the lesson's checks is written into the checkpoint folder's
files. It is experimental: related facts can disturb each other, recall drops past a few dozen facts and with thinking
on, and learned facts cost some decode speed on a Mac. Known issues
has the details.
tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-MLX-4bit
hub="${HF_HUB_CACHE:-${HF_HOME:-$HOME/.cache/huggingface}/hub}/models--TensorFold--NVIDIA-Nemotron-3.5-Lightning-30B-A3B-MLX-4bit"
src="$hub/snapshots/$(cat "$hub/refs/main")"
mkdir -p ~/models && cp -RL "$src" ~/models/nemotron-living # always teach a copy
(umask 077; openssl rand -hex 24 > ~/models/slide-key) # --slide starts only with a key
tensorfold serve ~/models/nemotron-living --slide --api-key-file ~/models/slide-keyLeave that server running and send a lesson from another terminal:
curl -N http://127.0.0.1:8080/v1/slide/learn -H "Authorization: Bearer $(cat ~/models/slide-key)" \
-H 'Content-Type: application/json' -d '{"text": "I like blue."}'Send it a document instead and it finds the facts in it and learns them one by one. Open
http://127.0.0.1:8080/slide to watch in 3D: the facts, the neurons each one took in the model's layers, the shard
files that hold them, and the lesson running now.
Learning runs on Apple-silicon Macs and on an NVIDIA GB10 (DGX Spark), and a folder that learned on one serves on the
other. On an APFS volume, cp -cRL makes a clone that shares the original's data until lessons change it. It rewrites the folder's files:
before the first lesson the server keeps the originals in a hidden snapshot, and
tensorfold slide rollback ~/models/nemotron-living puts them back, undoing every lesson. The
Living Weights guide covers the key, teaching files and web pages, omp's /learn command,
the 3D view, undoing lessons, and what to expect.
These are the native release's serving paths. A recipe may also describe the older Python 0.6.6 engine;
use its native section and the current changes together.
Hardware below records the documented scope, not an unmeasured minimum. Context, parallel requests, prompt caches
and drafters need memory beyond the weights. tensorfold info REPO reports the checkpoint's memory floor.
Where no RAM capacity is published, inspect that floor before serving a large checkpoint.
Run a table command after installation. On a Linux archive installation, use the tensorfold shell function from Quick start.
REPO@revision pins a pull; serve the repository name without the revision after pulling it.
Conversions are explicitly marked: their upstream weights need the linked conversion before native serving.
| Model / format | Download | Machine and memory scope | Native recipe / limits |
|---|---|---|---|
| Qwen3.5-2B, MLX 4-bit | tensorfold pull mlx-community/Qwen3.5-2B-MLX-4bit@93760be4f1f69842a46bc13dbdc0f19e291392a3 |
One Mac (M5 Max checked), or one GB10; no RAM minimum published. | 2B; CUDA release checks. Text only. |
| Qwen3.8-27B, MLX 4-bit | tensorfold pull TensorFold/Qwen3.8-27B-MLX-4bittensorfold pull z-lab/Qwen3.8-27B-DFlash2 |
One Mac (M5 Max/M3 Ultra), or one GB10. | 27B. Add --drafter z-lab/Qwen3.8-27B-DFlash2; without it decoding is plain. |
| Nemotron 3.5 Lightning 30B-A3B, MLX 4-bit | tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-MLX-4bit |
One Mac (M1-M5), or one GB10; also checked on RTX 3090 / RTX PRO 6000. | Lightning. Includes MTP. |
| Lightning, mixed oQ4 / oQ6 / oQ8 | tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-oQ4tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-oQ6tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-oQ8 |
One M3 Ultra checked. | Width and MTP instructions. Metal only; copy the 4-bit checkpoint's MTP head to draft. |
| Lightning, NVIDIA NVFP4 | tensorfold pull nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 |
One GB10 checked; cards without thread-block clusters (sm_80 to sm_89) use a fallback path. | Lightning; CUDA changes. |
| Nemotron 3.5 Super VL 120B-A12B, NVFP4 | tensorfold pull nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4@82d93699acd3be9e4617c53360fe5b3f5dbeb1b3 |
One or two GB10s; weights 70.8 GiB, 73.2 GiB with vision on one. | One/two-GB10 commands. Add --vision for images. |
| Nemotron 3.5 Bolt 74B-A9B | NVIDIA's checkpoint | Native CUDA; 24 GB checkpoint, checked on RTX PRO 6000. | Bolt/Omni video; release qualification. |
| Qwen3.8 Flash Next, MLX 6-bit | tensorfold pull TensorFold/Qwen3.8-Flash-Next-MLX-6bit-MTP |
One M5 Ultra checked; RAM minimum not published. | Metal, runbook. |
| Qwen3.8 Flash Next, MLX 4-bit | tensorfold pull TensorFold/Qwen3.8-Flash-Next-MLX-4bit-MTP |
One GB10; 73.3 GiB uploaded, plus mapped tables and caches. Metal has a native 4-bit path too (M3 Ultra checked). | Native CUDA. NVFP4/two-local-rank and vision paths are experimental. Metal's shared-session limits are in known issues. |
| GLM-5.3-Flash, MLX 4-bit | tensorfold pull TensorFold/GLM-5.3-Flash-MLX-4bit-MTP |
Two M5 Ultras; single-Mac native serving is not qualified. | Paired Metal runbook. Do not use the Python recipe's single-Mac command. |
| GLM-5.3-Flash, EXL3/TR3 | tensorfold pull Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw |
Two CUDA ranks; the release checks the two-GB10 setup. | Release scope, checkpoint. Experimental checkpoint; use native rank options, not the Python installation. |
| GLM-5.3-Flash, NVFP4 | Not documented yet. | Two GB10s at TP=2: @Bizuayeu loaded the whole checkpoint on 1.1.0 in 302.1 s and served it. | Contributed run: one token hash per prompt across three runs and a passphrase found in 199,307 tokens; scored prompts and more than two ranks were not covered. |
GLM-5.3, EXLR8 home |
tensorfold pull kindlingai/glm-5.3-exlr8-k2-k3-k4@f459f5d0272a529403dd9b3fd32f771f4250a0d4 --exlr8 home --node 0/2 --dir ~/models/glm-5.3-exlr8-home |
Two M5 Ultra Macs, 256 GB each; about 160 GB resident per Mac at 8k context / four streams. | Two-Mac recipe. Use --node 1/2 on the other Mac; Thunderbolt 5 and MCDMA required. No CUDA backend. |
| LFM2.5-24B-A2B, MLX 4 / 6 / 8-bit | tensorfold pull LiquidAI/LFM2.5-24B-A2B-MLX-4bit@667408d494bde191c2cb9dd4f4549827816b9275tensorfold pull LiquidAI/LFM2.5-24B-A2B-MLX-6bit@1deec53eb49571e21cdcbf82fd5839248f07ed88tensorfold pull LiquidAI/LFM2.5-24B-A2B-MLX-8bit@67ab417dd02d0c9545802a726df668620eae049d |
One Mac; M4 Pro checked. | Native operator recipe, release history. No separate native model recipe yet. |
| Qwen3.6-35B-A3B, MLX with MTP | tensorfold pull TensorFold/Qwen3.6-35B-A3B-MLX-4bit-MTP@81169a9bc511a27c1b4eedb77a2cd98ced431847 |
One GB10; 20.9 GB checkpoint. | Qwen3.6. Native CUDA; the recipe's Metal path belongs to Python. |
| Gemma 4 31B, MLX 4-bit | tensorfold pull mlx-community/gemma-4-31B-it-4bit@696d436c404745a59f30e4939a658162b0a9e57ftensorfold pull z-lab/gemma-4-31B-it-DFlash@eabd648301ce28583cc14757912e5e0f84e152e1 |
One M5 Mac; M5 Ultra checked. M1-M4 refused. | Native 31B. Name the drafter with --drafter; images with --vision. |
| Gemma 4 12B, converted MLX 4-bit | hf download google/gemma-4-12B-it --revision 707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7 --local-dir ~/models/gemma-4-12b-hfhf download google/gemma-4-12B-it-assistant --revision 46d4c6f13f0ac0ad827b915669b8df9b81c64c51 --local-dir ~/models/gemma-4-12b-assistant |
One M5 Mac; converted text weights 6.3 GB, image weights 100 MB, plus assistant/caches. M1-M4 refused. | Convert and serve. Upstream download needs hf; conversion needs Python, mlx and mlx-lm. Serving is native. |
| Step-5-Preview, converted affine | hf download stepfun-ai/Step-5-Preview --local-dir ~/models/step5-preview-fp8 |
One 256 GB Mac; text 166.9 GiB, MTP adds 2.5 GiB and vision 3.7 GiB. | Convert and serve. The documented 256 GB setup serves converted affine weights; converting the upstream FP8 download needs Python and mlx. |
| Liquid AI d1-3B, decisions | Not documented yet. | Native CUDA; RAM minimum not published. | API section below. /v1/systemone and /v1/decisions. |
| Fish Audio Drama 3, speech | Not documented yet. | Native Metal or CUDA; GB10 measured. | Release audio instructions. /v1/audio/speech; checkpoint and codec.pth required. |
The native release does not qualify GLM-5.3 on one machine. GLM-5.3 EXLR8 home needs two 256 GB M5 Ultra Macs;
its recipe gives both pull and serve commands. GLM-5.3-Flash is a different model:
its native Metal setup needs two M5 Ultras, and its native CUDA setup uses two ranks. The single-256-GB-Mac
GLM-5.3-Flash command in the older recipe is for Python 0.6.6, not this release.
For the NVFP4 two-GB10 path, see what the contributed run covered.
DeepSeek checkpoints are not served yet by any release binary: whole-model gate #827 is still open. MiMo's native backend is not in a release yet. Bonsai's ternary pack and Gemma 4 26B-A4B remain Python-only; they are not native download recommendations. Known issues lists current limits, including Flash Next shared-session cache/performance behavior and the 27B's drafter limits.
Every accepted draft must equal the token the same native engine would produce with "draft": false.
A resumed request must equal fresh execution, and each concurrent stream must equal its solo run.
The comparison fixes the checkpoint, backend, settings and runtime.
Different quantizations and different backends can produce different outputs.
Flash Next on Metal uses shared target rounds with an explicit --parallel N above one, when that many sessions fit.
A lone greedy request there uses GPU-side rounds and hands over when a second request arrives.
--parallel auto (the default) and --parallel 1, learning and two-Mac serving use the single-reply driver.
FZ_BATCH_MTP_SERIAL=1 restores serial MTP drafting on the shared host.
The earlier MTP chain receipt records its own pinned commits.
Nemotron, GLM, the 2B model and Qwen3.8-27B use shared lane rounds.
On Metal, each of up to 128 streams in the 27B's shared rounds drafts its own DFlash2 tree.
tensorfold pull TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-MLX-4bit
tensorfold models
tensorfold info TensorFold/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-MLX-4bitpull downloads a checkpoint into the Hugging Face cache and checks what it downloads against the Hub: large files by sha256, small files by their git blob sha1.
Large files come down in pieces over up to 16 connections, and an interrupted pull resumes with the pieces it lacks.
It refuses a checkpoint whose model_type has no native family, and fetches the 27B's DFlash2 drafter for --drafter. models lists the cached checkpoints with a model type it knows, and info shows one checkpoint's family, format, context and memory floor.
tensorfold serve REPO serves a cached checkpoint by its repository name.
Compaction is off by default, and with it off every reply is unchanged.
With --compact-at, a conversation that would overflow is compacted instead of refused.
The server keeps the system prompt and the most recent turns word for word, and never separates a tool call from its result.
The older turns become a memory note: goal, constraints, progress, decisions, next steps, exact names and numbers, and the files the tool calls read or changed.
Each later compaction updates the previous note instead of starting over. A client that sends back the compacted conversation keeps its note.
The reply carries the note and the compaction counts under tensorfold.compaction, and every reply reports context_window and context_used.
The same request gives the same compaction and the same reply. With --compact-memory, the stored note carries over between requests.
Off by default. --cortheon gives every chat request Cortheon's reasoning program and web research run by the server, so any OpenAI-compatible client gets it with no setup.
The model works through Cortheon's stages for its task type and can call cortheon_search and cortheon_fetch, which the server runs without the client seeing them.
A question about current facts, or a request to build with an outside library, is researched before the model starts.
A coding task that changed files is asked to run its tests before it finishes.
Web text is filtered for instructions aimed at the model, and reads reach public addresses only.
With the flag, search queries written from the conversation go to DuckDuckGo, or to Brave with BRAVE_SEARCH_API_KEY, and pages are fetched from this machine; without it, the server makes no searches and reads no pages.
See Cortheon mode.
GET /v1/models (also /models) advertises each served name and alias. When the engine reports a finite
context window, each entry includes equal context_length and max_model_len integer extensions. These
are the loaded engine's effective prompt-plus-reply token limit, including any startup memory fitting;
they are not the default reply limit. An unspecified engine window omits both fields. Clients should
still handle per-request memory refusals: an advertised context is not a guarantee that every workload fits.
A model that thinks also carries an OpenRouter-style reasoning: default_enabled, default_effort (only
with --reasoning-effort), and supported_efforts, the levels its template names, highest first, then none
when the template has a thinking switch. A template that names no level leaves supported_efforts out. These
describe chat and Responses requests.
The read-only /memory diagnostics report process footprint and lifetime peak,
available backend allocation counters, and the applied retained-prefix plan without changing memory policy.
The binary's capabilities --json response lists its supported flags and platform-specific values.
serve MODEL --help prints usage without loading a model.
| Flag | Meaning |
|---|---|
--host HOST, --port PORT |
Listen address and port, default 127.0.0.1:8080. |
--name NAME |
Model ID advertised to clients. |
--alias NAME |
Another accepted model ID; repeat for multiple aliases. |
--api-key KEY |
Require a bearer key; repeat for multiple keys. |
--api-key-file FILE |
Read keys from a restricted-permission file. |
--metrics-open |
Allow /metrics without a key when API authentication is enabled. |
--dashboard |
Enable the local /dashboard page and /stats endpoint. |
--context N |
Bound prompt plus reply tokens; the model's window and memory checks still apply. Qwen3.8-27B defaults to 32,768 and takes up to 262,144. |
--speed-up FILE |
Rank and link settings for two-Mac Flash Next or GLM serving. |
--max-tokens N |
Default reply limit, 4096; requests can override it. |
--temperature T |
Sampling temperature; zero requests greedy decoding. |
--top-p P, --top-k K, --min-p P |
Sampling defaults. |
--thinking, --no-thinking |
Choose whether the chat template opens a reasoning block. |
--reasoning-effort LEVEL |
Default template effort: low, medium, high or xhigh. |
--thinking-budget N |
Limit reasoning tokens where the engine supports closing the reasoning block. |
--loop-guard |
Close a short repeated reasoning cycle where the engine supports it. |
--no-drafts |
Produce the plain reference through the same engine. |
--drafter DRAFTER |
An external drafter, as a directory or a repo id in the Hugging Face cache, for models that take one: Qwen3.8-27B uses DFlash2 and Gemma 4 31B the drafter named in its recipe. Without one they decode plain. |
--drafter-bits 0|4 |
Drafter weight precision where the model offers a choice: 4 bits (the default), or 0 to keep bf16. Qwen3.8-27B on CUDA takes 4 only. |
--record DIR |
Write each stream's committed tokens, positions and tapped rows under DIR, for drafter training. CUDA Nemotron only; a single stream runs through the lane rounds, and recording stops below 25 GiB free disk. |
--keep-warm SECONDS |
Keep the Metal GPU active while idle for this long after a request, default 900; zero disables it. |
--parallel N |
Admit up to N requests where the engine shares lanes; auto is the default. |
--prompt-cache-gib GIB |
Retained-prefix budget, 16 GiB by default or less where memory is short; 0 disables it. |
--prompt-cache-over-cap |
Permit an explicit prefix budget past what 70% of RAM leaves once the server loads. |
--learn |
Keep shared prompt prefixes on disk for new conversations to resume; the models are below the table. |
--learn-dir DIR |
Where --learn keeps them, ~/.cache/tensorfold/learned by default; implies --learn. |
--learn-gib GIB |
Disk for learned prefixes on each Mac, 32 by default; the least recently used go first. Implies --learn. |
--learn-min-free-gib GIB |
Free disk --learn leaves on each Mac, 4 by default; below it learning pauses and serving goes on. |
--learn-conversations |
Keep every prompt state on disk too, each conversation's and the shared prefixes, in one store in place of --learn's, so a next turn after a restart, a crash or an eviction reads it back. Implies --learn; see Conversations on disk. |
--learn-daily-gib GIB |
What --learn-conversations may write to disk a day, every model together, 32 by default; past it states stay in RAM only. |
--recall |
Draft from a record of the token ids earlier requests read and wrote, so repeated text is verified in a few rounds; replies are unchanged. Off by default; see Recall for what stays on disk. |
--recall-dir DIR |
The record's directory, one a tokenizer under ~/.cache/tensorfold/recall by default; implies --recall. |
--recall-gib GIB |
Disk for the record, 1/16 GiB by default; the oldest ids go first. Implies --recall. |
--recall-read-only |
Draft from the record without adding to it, as gates and benchmarks run; implies --recall. |
--snapshot-dir none |
Keep prefix state in memory; none is the supported value. |
--max-snapshots 0 |
Disable disk snapshots at startup; 0 is the supported value. |
--compact-at auto|FRACTION |
Turn on context compaction (below). auto compacts when the prompt and reply would pass the window minus a reserve. |
--compact-keep TOKENS |
Recent tokens kept word for word when compacting; the default is 20,000 or a quarter of the window, whichever is smaller. |
--compact-memory DIR |
Also keep each conversation's memory note as a Markdown file in DIR. |
--cortheon |
Cortheon's reasoning program and server-run web research for chat requests; see Cortheon mode. |
--no-update-check |
Accepted compatibility switch; update the installed binary through its package manager. |
--backend auto |
Use the backend compiled for the platform; mlx selects native Metal on macOS and cuda selects CUDA on Linux. |
--prompt-cache-gib applies to Nemotron, Flash Next, GLM, Qwen3.8-27B and LFM2.5-24B-A2B, and
--prompt-cache-over-cap to the same models but GLM.
--learn keeps shared prompt prefixes, such as a system prompt and its tools, on disk, so new conversations resume
them after a restart or an upgrade that computes the same bits. It covers GLM-5.3-Flash, Nemotron, and Flash Next on
one Mac; Qwen3.8-27B, Qwen3.5-2B, LFM2.5-24B-A2B, GLM-5.3 (EXLR8) and a CUDA server refuse it at startup.
Sampling defaults come from the checkpoint's generation_config.json, then serve flags and request fields override them.
--context 0 is family-specific; use a positive limit for GLM and inspect the capacity reported at startup.
The supported environment variables are listed in capabilities --json, including API keys, request logging and Hugging Face cache paths.
tensorfold benchmark MODEL runs public code and chat fixtures on a cached model and saves a portable receipt;
--publish optionally submits it for review at tensorfold.dev. See benchmark commands and measurement definitions.
Use the Zig version pinned in .zig-version, currently 0.17.0.
On a Mac with Xcode's Metal toolchain:
zig build native -Dcpu=apple_m1
zig build test test-golden -Dcpu=apple_m1On macOS, test also runs available Metal device checks. See CONTRIBUTING.md for the host-test subset.
The server is zig-out/native/bin/tensorfold-native.
Release archives include the native executable, runtime assets and license notices; packaging describes the qualified CUDA inputs.
For cache-aware performance receipts, see observed cache benchmark evidence.
The clients retain server usage and output fingerprints, and verify cold/reused states rather than inferring them.
Read CONTRIBUTING.md for exactness, precision and performance gates.
Coding agents should also load docs/recipes/SKILL.md; agents that support skills can install it as tensorfold-contributor.
TensorFold is Apache-2.0; see LICENSE, NOTICE and THIRD_PARTY_NOTICES.md.
Thank you to everyone who has sent TensorFold a pull request, a measurement or a bug report:
@0mao0, @321sssrt-bit, @aditya1503, @AdrianBinDC, @akol1, @Alexbob0, @anvilsong, @Arminova, @b-ostrov, @BadAd84, @barelyworkingcode, @Benjamin-Wegener, @benthecarman, @benwilson, @BHCC2025, @Bizuayeu, @BlivionIaG, @BobClawblaw, @borodach23, @Boscoeuk, @boxabirds, @brandonmmusic-max, @bunnyfu, @CerebralCoding, @cesarswong, @chadhurley25075-png, @chaog992, @Charlie-Louis, @Chedrian07, @chris247474, @christrade215, @crescit, @cshintov, @cwschroeder, @DakotaTexas, @danyo1399, @Deesha08, @Defilan, @DevRico003, @di37, @drowzeys, @ecohash-co, @edurdias, @eleqtrizit, @ericlsimplifi, @EugeneClaw, @feni6, @gbgbgbg, @GDACONSULT, @gecobattya, @gilby, @Gogo6969, @gprot42, @GraithSecurity, @grantoverton, @grearjake-star, @greatyingzi, @GustavBlack, @harrisonfriia, @haxudev, @heitke, @hichaiuse, @ivanfioravanti, @jasontitus, @jayleaton, @jeffpeng3, @jeidbugs404, @jetnet, @jkuepker, @johnymoo, @JordiPosthumus, @JRaxworthy, @jregan-beasley-smc, @jschmied, @juliankang4, @kelchm, @kingjamez, @kky42, @lcgutierrez, @LECYWZA, @lijian1999, @liumorrisclaw, @LXD-8, @m-naoki-m, @mapamalu, @mcclanahanaman, @MESevenJourney, @mgoldwasser, @MiaAI-Lab, @mikolaj92, @millaguie, @Mirrdhyn, @Moutonc, @MovieMaker93, @mrpmorris, @mrv777, @MV10, @NeoAiLabs, @Nipale-ai, @nood-co1, @nullburn, @olexale, @omar16100, @optimisme, @otarkhan, @outcastofmusic, @paragontasx, @peacockesq, @philip-pentatonic, @plotarmordev, @pmeenan, @pulseandthread, @quigles1977, @rafafortes, @raymondkpwong, @robertpitt, @RoscoeTT, @RS91Dev, @salmanarshad321, @samwang0041-star, @sanjaibalajee, @satindergrewal, @scottleimroth, @sethforprivacy, @sfxnz, @shantanugoel, @simon-lin88, @simonmd, @spenchey, @squarrier, @ss-cong, @styles01, @SvangenStudios, @SxMShaDoW, @sxuff, @taussoe, @tfolkman, @ThinkOffApp, @Thotheris, @tinyapps, @tolewis, @tomByrer, @tonydehnke, @tournierjc, @tpischke, @urtho, @vcruz305, @vinicius-symetrix, @wojo, @xjqx2z, @Yuepixel, @YvesLaRose, @ZackSample, @zweck.
docs/credits.md names whose Python engine work each part of the native engine carries.
Usable memory profiles explains how to record applied budgets, context, cache state and process peaks.