Models › xor family

juspay Apache 2.0 out 34 days ago

35.1B parameters (8 of 256 experts per token), text in, text out, 256K tokens of context.

Verified 2026-10-25 Rank #9075 in Decision models · score 41 method v1 Sources 3 Downloads 30d 1.9k · #579 of 619 tracked

Can I run it?21GB at INT4Estimatefits a 24 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?1.9kdownloads, 30 days#579 of 619 we track · below the median+1 likes, downloads −39% this week56 likesHugging Face2026-10-24

Worth reading

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Most discussed · 134 HN pts

Show HN: I built a toy TPU that can do inference and training on the XOR problem

Tiny-TPU is a custom accelerator that re-invents Google's TPU logic by scaling down the original 256x256 systolic array to a 2x2 grid.

  • The team adopted a strict philosophy to ALWAYS TRY THE HACKY WAY before consulting external sources.
  • The project explicitly aims to code without relying on AI to write for us.
tinytpu.com 2025-08-18 134 HN pts · 24 comments 5.7k words

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 7 Spaces.

  • As a service0no API provider listed
  • On own hardware0quantized builds
  • Built on00 fine-tunes · 0 adapters · 0 merges
  • In apps7Hugging Face Spaces

Top Spaces running it: multimodalart/jev-decision-index ♥ 802

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 64 GB Mac (unified memory) at INT4

About 21 GB at INT4, 42 GB at FP8, 84 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 21 GB of weights (estimate; KV cache and activations extra)INT421 GBFP8: about 42 GB of weights (estimate; KV cache and activations extra)FP842 GBBF16: about 84 GB of weights (estimate; KV cache and activations extra)BF1684 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 21 GB of weights (estimate; KV cache and activations extra)INT421 GBFP8: about 42 GB of weights (estimate; KV cache and activations extra)FP842 GBBF16: about 84 GB of weights (estimate; KV cache and activations extra)BF1684 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 23 repo commits so far

Teal is the weights (2026-09-21), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

SepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-09-28: 1 repo commit2026-09-27: 1 repo commit2026-09-26: 2 repo commits2026-09-23: 3 repo commits2026-09-22: 1 repo commit2026-09-21: 15 repo commitsShow HN: I built a toy TPU that can do inference and training on the XOR problem · 2025-08-18Weights 2026-09-21 · 2026-09-21Weights 2026-09-21now
SepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-09-28: 1 repo commit2026-09-27: 1 repo commit2026-09-26: 2 repo commits2026-09-23: 3 repo commits2026-09-22: 1 repo commit2026-09-21: 15 repo commitsShow HN: I built a toy TPU that can do inference and training on the XOR problem · 2025-08-18Weights 2026-09-21 · 2026-09-21Weights 2026-09-21now

Reference

Everything else, folded: the full tables, every source.

Benchmarks

11 rows

Benchmarks 11 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (11)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported Brier mean0.18All publicmodel author2026-10-07
author-reported ECE0.07All publicmodel author2026-10-07
author-reported JEVBench1Easymodel author2026-10-07
author-reported JEVBench0.96Originalmodel author2026-10-07
author-reported JEVBench0.82Hard publicmodel author2026-10-07
author-reported JEVBench0.9All publicmodel author2026-10-07
author-reported Operational success1All publicmodel author2026-10-07
author-reported coverage1All publicmodel author2026-10-07
author-reported macro accuracy0.91All publicmodel author2026-10-07
author-reported schema validity1All publicmodel author2026-10-07
author-reported strict schema validity1All publicmodel author2026-10-07

Use-case evidence

6 use cases

What it is good at 6 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in Decision models · LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesQwen3_5MoeForConditionalGeneration
Model typeqwen3_5_moe
Dtypebfloat16
Params by dtypeBF16: 35,107,181,936
Size bandXL
MoEyes
Experts256
Experts per token8
Context length262,144 (config.json max_position_embeddings)
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Qwen3_5MoeForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Qwen3_5MoeForConditionalGeneration · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for xor: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)84 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)42 GB · Single high-end GPU (<80 GB)
INT4 (0.5 bytes/param)21 GB · Single GPU (<24 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

Decision facts

catalog

Decision-model catalog curated

How it was madefull fine-tune (whole weights published)
Lineage bucketF - fine-tune of an open model (public/synthetic data)
Lineage evidenceboard file inspection (adapter/head/weight files) + HF base_model field
Base modelQwen/Qwen3.6-35B-A3B
DI 0.2.1 score41.48 (independent (DI 0.2.1 board))
ECE (board)0.0149
Median latency139.9 ms (DI board, 1x RTX PRO 6000, single process)
Hosted price (input)self-hosted/M
Sourcehttps://huggingface.co/juspay/xor

All variants

lineage

Lineage

Fine-tuned from Qwen/Qwen3.6-35B-A3B Family: juspay/xor 0 derivatives on the Hub

Base model: Qwen/Qwen3.6-35B-A3B

Repository changelog

23 commits

Repository changelog 23 commits

DateCommitSHA
2026-09-28 Release Xor 1.2 a286a46
2026-09-27 Update README.md 8d2d708
2026-09-26 Clarify media limits in model card ace46ec
2026-09-26 Release Xor 1.1 a21c92d
2026-09-23 Rename JevOne to Xor 679decd
2026-09-23 Document multimodal serving support 8a03d38
2026-09-23 Update multimodal serving wrapper be42456
2026-09-22 Update model card with post-training and benchmark details 362f60b
2026-09-21 Update serving bundle checksum 4de0db7
2026-09-21 Fix host port publishing 6f1839d
2026-09-21 Remove superseded jev-trained serving artifacts fc03bb6
2026-09-21 Rename model to JevOne bdac774
2026-09-21 Add JevOne serving checksum a247e96
2026-09-21 Add JevOne serving bundle 4210cec
2026-09-21 Upload serving/jev-trained-serving.tar.gz.sha256 with huggingface_hub 93f1a30
2026-09-21 Upload THIRD_PARTY_NOTICES.md with huggingface_hub 0d4be71
2026-09-21 Upload LICENSE with huggingface_hub 7ff7442
2026-09-21 Update README.md 7cd68c6
2026-09-21 Upload README.md with huggingface_hub 3130c43
2026-09-21 Upload checksums.sha256 with huggingface_hub aab97aa
2026-09-21 Upload serving/jev-trained-serving.tar.gz with huggingface_hub 1f5fc56
2026-09-21 Upload folder using huggingface_hub afa42a3
2026-09-21 initial commit def428d

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Analysis1

tinytpu.com2025-08-18HN front pageHN 134 pts · 24 comments5,690 words
Tiny-TPU is a custom accelerator that re-invents Google's TPU logic by scaling down the original 256x256 systolic array to a 2x2 grid. The design uses Processing Elements (PE) connected in a grid-like structure where each PE performs a multiply-accumulate operation. Data flows through these elements to calculate matrix multiplications that dominate 80-90% of the compute operations in transformers.written, reviewed, cited
  • The team adopted a strict philosophy to ALWAYS TRY THE HACKY WAY before consulting external sources.
  • The project explicitly aims to code without relying on AI to write for us.

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:43 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:17 UTC · Last verified 2026-10-25

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