shad0w

Compile the decisions your LLM already makes into a certified 2 MB table that answers in microseconds on a CPU.
It learns from your model's own answers, needs no labels, and tells you in writing how often it will disagree.

Python

Live demo · Docs · 2-minute video · Benchmarks

Your app sends small, typed decisions to a big model millions of times: which intent is this, which queue gets this ticket, is this spam, should this agent step run. Every call takes 20–1,500 ms and a round trip to someone else's GPU.

shad0w sits in front of that model like a shadow. It watches the answers, learns the decision, and compiles it into a tiny integer table. Before the table answers anything, it certifies how often it will disagree with your model. On public intent and topic benchmarks, it then served 45–71% of traffic itself, at under 5% certified disagreement and in microseconds. Everything else goes to your model, exactly as before.

import shad0w

sh = shad0w.Shadow("bundle/", teacher=ask_my_llm, log="teacher_log.jsonl")

sh.decide("my card was stolen yesterday")
# Decision(answer='lost_card', source='table', confidence=0.98, certified=True, latency_us=14)

~1 µs
_{per decision in C
7–11 µs JS · 15 µs Python}

45–71%
_{of LLM traffic served locally
at a certified 5% bound}

0 labels
_{learns from your model's
own logged answers}

2 MB
_{one file, no GPU, no network
256 KB still keeps 88.5%}

How it works

shadow → compile → certify → serve

Shadow. Your model keeps serving. Its answers are logged as (text, answer) pairs, either by the Shadow wrapper or by your own logging.
Compile. The log trains a hashed n‑gram table with int8 weights. There is no tokenizer and no GPU, and it takes seconds to minutes on a laptop.
Certify. On a held-out slice, a Learn‑then‑Test procedure picks the confidence threshold. Among answers the table serves, disagreement with your model is ≤ α (say 5%), with probability ≥ 1 − δ.
Serve. Certified answers come back in microseconds, in your process. Everything else goes to your model and is logged, so the share the table can take grows with traffic: 31% → 70% as AG News shadow traffic went from 2k to 100k.

Results

Measured on public datasets. Every number links to its raw result file on the benchmarks page.

	Result
LLM traffic answered locally (7 intent/topic tasks, 2 teachers), certified α = 5%	45–71%, realised disagreement 3.9–5.7% (mean of 3 seeds)
End-to-end accuracy (table + teacher) vs teacher alone	within 1 point on every task; +0.7 with Kev‑0.8B as teacher
Mean latency of the cascade, Kev‑0.8B → BANKING77	75 ms → 22 ms
Offload as shadow traffic grows (AG News, 2k → 100k decisions)	31% → 70% at α = 5%; 5% → 52% at 2%
Per decision	~1 µs C · 7–11 µs JavaScript · 15 µs Python · 0.19 ms over HTTP
Public benchmark (local‑jev‑bench, BANKING77, 20 options)	89.7% at 0.018 ms on 1 CPU thread; Kev‑0.8B 88% at 57.6 ms; Clef‑flash (9B) 97% at 1,274 ms
Out-of-scope queries (CLINC150) separated by confidence	AUROC 0.94
Reproduce on your laptop (`examples/shadow_demo.py`, MIT-licensed teacher, BANKING77)	69.4% answered locally at α = 5% (realised 4.6%); cascade 67.8% vs teacher 68.0%

Compared with what you would otherwise do

	Per decision	Runs on	Needs	Bound on what it serves
Keep calling the LLM / decision API	20–1,500 ms	GPU or vendor API	—	it is the reference
Semantic cache (GPTCache, gateway caches)	ms (embed + vector search)	embedding model + store	nothing	similarity threshold, no bound
Embedding router (e.g. semantic-router)	ms	embedding model	example utterances	threshold, no bound
Distil into a smaller LLM	10–100 ms	GPU	teacher outputs	none
Hand-built fastText / SetFit / Model2Vec	µs–ms	CPU	human labels	none
shad0w	1–15 µs	CPU, in-process, browser, edge	your model's logged answers	finite-sample, against your model

It is not new science. The certificate is Learn‑then‑Test, in the spirit of Trust or Escalate and BARGAIN. The table is fastText-shaped on purpose. What is new is the packaging: one workflow from shadow logs to a certified, KB-to-MB artifact, with the same decisions in C, Python and JavaScript.

Quick start

pip install "shad0wllm[compile]"        # the runtime alone is: pip install shad0wllm   (numpy only, C core included)

The package installs as shad0wllm and imports as shad0w. The command-line tool is shad0w.

1. Log your model's answers. Wrap the call you already make. With no bundle yet, every call goes to your model and is logged:

import shad0w

sh = shad0w.Shadow(None, teacher=ask_my_llm, question="intent", log="teacher_log.jsonl")
answer = sh.decide(text).answer          # = ask_my_llm(text), logged

2. Compile and certify once you have 1,000+ lines (10k–100k for high offload):

shad0w init                                        # starter schema.json: the options your model chooses from
shad0w shadow --schema schema.json --data teacher_log.jsonl --teacher my-llm-v3 --alpha 0.05 --out bundle/
shad0w report --bundle bundle/                      # the certificate, in plain words and numbers

3. Serve. Point the wrapper at the bundle. Certified answers come from the table and the rest from your model. 1% of certified answers are spot-checked against your model, so sh.stats() shows live disagreement:

sh = shad0w.Shadow("bundle/", teacher=ask_my_llm, log="teacher_log.jsonl")

4. Re-certify on fresh traffic every week, or when traffic changes:

shad0w certify --bundle bundle/ --data last_week.jsonl

Everywhere else


JavaScript (Node, browsers, Workers, Deno, Bun), zero dependencies	`npm i shad0wllm` → `const b = await Bundle.load("bundle/"); b.decide(text)`
HTTP sidecar, any language	`shad0w serve --bundle bundle/` → `POST /v1/decide`
C, any language with an FFI	`shad0w/_native/reflex.c`: `s0_load`, `s0_decide`
Decorator	`@shad0w.cascade("bundle/")` on your existing classify function
Human labels instead of a teacher	`shad0w compile --data labels.jsonl` + `shad0w calibrate` (~300 labels)
No teacher, no labels	`shad0w compile --unlabeled logs.txt` (option names + raw logs, `pip install "shad0wllm[logs]"`)

Integration recipes for FastAPI, LiteLLM, LangGraph, Cloudflare Workers and the browser are in the docs.

When not to use it

The limits below are measured. Knowing them is what makes the certificate useful.

Sentiment, tone, emotion. A hashed table tops out near 79% on SST‑2 even with perfect labels. shad0w certifies almost nothing there and defers everything to your model, which is the correct behaviour.
When you need the most accurate answer. A 9B decision model is 7 points better on the public BANKING77 file, at ~70,000× the latency. Put shad0w in front of such a model, not instead of it.
The bound is against your model, not the truth. A certified table in front of a wrong model is wrong in the same way. For a bound against the truth, calibrate with ~300 real labels.
The bound holds on traffic like the calibration slice. A big shift in the request mix can break it. The drift guard flags confidence shifts, the audits watch live disagreement, and shad0w certify re-certifies.
Reasoning or world knowledge. It learns surface decisions: intents, routes, topics, policies with distinctive wording.

Documentation

Getting started · Shadow mode guide · Integrations · The certificate, explained · API & CLI · Architecture & file format · Benchmarks · Limitations · FAQ

Contributing

Run it on one of your decisions and share your numbers, including failures. That is the most valuable contribution. Code contributions: see CONTRIBUTING.md.

Citation

@software{chauhan2026shad0w,
  author = {Chauhan, Gaurav},
  title  = {shad0w: certified microsecond decisions compiled from the model you already run},
  year   = {2026},
  url    = {https://github.com/2796gaurav/shad0w}
}

Built by Gaurav Chauhan · Apache-2.0

Metadata

Release files for shad0wllm 0.1.1

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Source distribution for shad0wllm 0.1.1
File	Size	Uploaded
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Built distributions (wheels)

Table of built distributions (wheels) for shad0wllm 0.1.1
File	Interpreter	ABI	Platform	Reset
shad0wllm-0.1.1-cp313-cp313-win_amd64.whl 55.2 kB Oct 7, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
shad0wllm-0.1.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 68.0 kB Oct 7, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
shad0wllm-0.1.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 69.4 kB Oct 7, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64	Details
shad0wllm-0.1.1-cp313-cp313-macosx_11_0_arm64.whl 52.8 kB Oct 7, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
shad0wllm-0.1.1-cp313-cp313-macosx_10_13_x86_64.whl 52.5 kB Oct 7, 2026	CPython 3.13	CPython 3.13	macOS 10.13+ x86-64	Details
shad0wllm-0.1.1-cp312-cp312-win_amd64.whl 55.2 kB Oct 7, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
shad0wllm-0.1.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 67.9 kB Oct 7, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
shad0wllm-0.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 69.3 kB Oct 7, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64	Details
shad0wllm-0.1.1-cp312-cp312-macosx_11_0_arm64.whl 52.8 kB Oct 7, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
shad0wllm-0.1.1-cp312-cp312-macosx_10_13_x86_64.whl 52.5 kB Oct 7, 2026	CPython 3.12	CPython 3.12	macOS 10.13+ x86-64	Details
shad0wllm-0.1.1-cp311-cp311-win_amd64.whl 55.2 kB Oct 7, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 67.8 kB Oct 7, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 69.2 kB Oct 7, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64	Details
shad0wllm-0.1.1-cp311-cp311-macosx_11_0_arm64.whl 52.8 kB Oct 7, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
shad0wllm-0.1.1-cp311-cp311-macosx_10_9_x86_64.whl 52.6 kB Oct 7, 2026	CPython 3.11	CPython 3.11	macOS 10.9+ x86-64	Details
shad0wllm-0.1.1-cp310-cp310-win_amd64.whl 55.2 kB Oct 7, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 67.7 kB Oct 7, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 69.1 kB Oct 7, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64	Details
shad0wllm-0.1.1-cp310-cp310-macosx_11_0_arm64.whl 52.8 kB Oct 7, 2026	CPython 3.10	CPython 3.10	macOS 11.0+ ARM64	Details
shad0wllm-0.1.1-cp310-cp310-macosx_10_9_x86_64.whl 52.6 kB Oct 7, 2026	CPython 3.10	CPython 3.10	macOS 10.9+ x86-64	Details

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Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

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Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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SHA-256 checksum: d07030758d18cbaab55fa81cdeb96ac270e368c3620f783309a5f886f534b249 Verifying attestations

Release files / shad0wllm-0.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl

Download URL	shad0wllm-0.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 6a064f47979b9b9e2cb61e36b7ece24fa33cefc196a3c60f9359f15facebe2e7 Verifying attestations

Release files / shad0wllm-0.1.1-cp312-cp312-macosx_11_0_arm64.whl

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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

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Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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Release files / shad0wllm-0.1.1-cp312-cp312-macosx_10_13_x86_64.whl

Download URL	shad0wllm-0.1.1-cp312-cp312-macosx_10_13_x86_64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp312-cp312-macosx_10_13_x86_64.whl
SHA-256 checksum: a6aa955561e33d499dfba87bee72a6ddf4572c3395001c030ac86f2025c00676 Verifying attestations

Release files / shad0wllm-0.1.1-cp311-cp311-win_amd64.whl

Download URL	shad0wllm-0.1.1-cp311-cp311-win_amd64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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SHA-256 checksum: b9e82b83f4db6733495ad546d9536c07756e0703de2795395aa8a6a9118f2a57 Verifying attestations

Release files / shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: 28dffe8ed1b3ba6b3777019924068aadd41ae958b5ed6ce834410d66c970be5a Verifying attestations

Release files / shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl

Download URL	shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Size	69.2 kB
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 295a853c78532cff0c0863a608feb6a81dd7fd01b6b1a44e05d534464c21a8e0 Verifying attestations

Release files / shad0wllm-0.1.1-cp311-cp311-macosx_11_0_arm64.whl

Download URL	shad0wllm-0.1.1-cp311-cp311-macosx_11_0_arm64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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SHA-256 checksum: 7f8d540b2271615f4ca67b22c0cdfef18906191b9274031ae16ec9d4e109f08b Verifying attestations

Release files / shad0wllm-0.1.1-cp311-cp311-macosx_10_9_x86_64.whl

Download URL	shad0wllm-0.1.1-cp311-cp311-macosx_10_9_x86_64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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Release files / shad0wllm-0.1.1-cp310-cp310-win_amd64.whl

Download URL	shad0wllm-0.1.1-cp310-cp310-win_amd64.whl
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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SHA-256 checksum: f1ca1f68de410bbee7d391e634c4e1aee5dcd02488336fe6d0ffb50903541712 Verifying attestations

Release files / shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size	67.7 kB
Tags	CPython 3.10 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

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SHA-256 checksum: 53b52b4d330e203544f5bec6644bcd87d8745a667df9323b66163b8d6c92e82e Verifying attestations

Release files / shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl

Download URL	shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Size	69.1 kB
Tags	CPython 3.10 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 697859de8831c2141934c051208f2602cfa209e54c1d8cf190cd162bf5e795a7 Verifying attestations

Release files / shad0wllm-0.1.1-cp310-cp310-macosx_11_0_arm64.whl

Download URL	shad0wllm-0.1.1-cp310-cp310-macosx_11_0_arm64.whl
Size	52.8 kB
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp310-cp310-macosx_11_0_arm64.whl
SHA-256 checksum: b4cda3ac844b9e3b211588f7907af7ef5a37399e06a4235b000cb10904d08484 Verifying attestations

Release files / shad0wllm-0.1.1-cp310-cp310-macosx_10_9_x86_64.whl

Download URL	shad0wllm-0.1.1-cp310-cp310-macosx_10_9_x86_64.whl
Size	52.6 kB
Tags	CPython 3.10 macOS 10.9+ x86-64
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Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/2796gaurav/shad0w
Publishing commit: github.com/2796gaurav/shad0w/tree/b158b04a3884f0065346d5c05ae58824b526bb09

Workflow

Publishing configuration: github.com/2796gaurav/shad0w/blob/b158b04a3884f0065346d5c05ae58824b526bb09/.github/workflows/release.yml
Publishing logs: github.com/2796gaurav/shad0w/actions/runs/37587362333/attempts/1

Artifact

Subject: shad0wllm-0.1.1-cp310-cp310-macosx_10_9_x86_64.whl
SHA-256 checksum: 96c108ffc09ec1169f26825368f10cd61fab0d066a8b05c7840c57b79e3a2c4c Verifying attestations