---
title: Clear-Quasar-230k
canonical_url: "https://www.modelscope.ai/models/silx-ai/Clear-Quasar-230k"
md_url: "https://www.modelscope.ai/models/silx-ai/Clear-Quasar-230k.md"
repository: silx-ai/Clear-Quasar-230k
last_updated: 2026-08-06
license: other
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 96
stars: 0
tags:
  - model-provenance
  - mixture-of-experts
  - experimental
---

# Clear-Quasar-230k

> Clear-Quasar-230k - An open-source model by silx-ai on ModelScope. Quasar-230K — Model Lineage and Audit Record

silx-ai/Clear-Quasar-230k is a machine learning model on ModelScope. licensed under other.

- **Repository**: silx-ai/Clear-Quasar-230k
- **License**: other
- **Tags**: model-provenance, mixture-of-experts, experimental
- **Downloads**: 96
- **Stars**: 0
- **Last updated**: 2026-08-06

Source: https://www.modelscope.ai/models/silx-ai/Clear-Quasar-230k

---

# Quasar-230K — Model Lineage and Audit Record

This repository is the public technical disclosure for the private
`silx-ai/Quasar-230K` weight repository. It records the checkpoint’s
initialization, transformations, later optimizer work, evaluation limitations,
and reproducible audit results.

## Model lineage

Quasar-230K uses `inclusionAI/Ling-mini-base-2.0-20T` as its disclosed
sparse-MoE/base initialization. Quasar adds a custom hybrid-attention system
using Quasar, Raven, and GLA branches.

This lineage must not be interpreted as from-scratch or independently sourced
pretraining. Weight inspection can establish stored differences and functional
changes, but it cannot authenticate a historical step count or training-token
count by itself.

## Early coordinate conversion

An early checkpoint-conversion experiment expressed inherited tensors in a
different, functionally equivalent coordinate basis. The recorded operations
included:

- paired RMSNorm/projection sign changes;
- residual and intermediate-channel permutations;
- group-local physical expert-storage permutations with preserved logical
  routing;
- common per-layer router-bias shifts that preserved top-k ordering.

The experiment explicitly referenced `inclusionAI/Ling-mini-base-2.0-20T` and
used absolute raw weight-cosine targets of `0.02` for selected tensors and
`0.2` for RoPE-compatible Q/K signs. These transformations were designed to
preserve behavior. They are not additional pretraining and are not evidence of
provenance independence.

The exact historical sign vectors, permutations, seeds, router shifts, source
hashes, and expert-storage maps are retained in the sealed provenance evidence.

## Canonical expert storage

The final packed expert tensors were physically rewritten into canonical
logical order. Logical router expert ID now equals physical expert index, so a
runtime expert permutation map is no longer required.

Canonicalization performed no optimizer update. It rewrote 38 packed expert
tensors across 19 shards and verified:

- all 38 canonical expert tensors against the parent’s logical experts;
- 523 non-expert tensors in rewritten shards as bit-exact to the parent;
- the twentieth weight shard as unchanged;
- absence of the runtime expert-storage permutation contract.

The four-GPU EP4 ten-token sentinel generated token ID `1594` at all ten steps
both before and after canonicalization. Top-two token identities matched on all
ranks at every step. Eight of ten steps had identical reported BF16 top-two
values. The other two differed by at most `0.875`; an unchanged-parent control
rerun showed the same two-step variation and maximum delta under the same
distributed grouped-GEMM kernel.

## Expert optimizer work

The final experts are not merely reordered or sign-flipped copies of the
initial experts. An exhaustive audit covering all 19 × 256 = 4,864 experts,
after applying the documented historical inverse alignment, reported:

| Measurement | Result |
|---|---:|
| Aggregate magnitude cosine | `0.685631116` |
| Highest recovered expert cosine | `0.753024142` |
| Relative Frobenius error | `0.856687561` |
| Exact inverse-aligned element fraction | `0.124332%` |
| Fully exact expert components | `0` |

Part of the expert work used an explicitly disclosed Ling-referenced,
inverse-aligned weight-space objective. The final fast expert stage did not
perform a language-model/data forward pass. It must be described as
source-aware expert weight-space optimization, not independent pretraining.

## Quasar-specific branch training

The Quasar-specific branch system contains 298,310,800 parameter slots
(`1.802%` of the checkpoint). All 107 large branch matrices received optimizer
gradients during the documented branch-training stage.

| Measurement | Result |
|---|---:|
| Aggregate magnitude cosine versus branch starting state | `0.886760701` |
| Relative Frobenius error versus branch starting state | `0.553353038` |
| Exact unchanged element fraction | `0.304765%` |
| Held-out causal loss with branches active | `7.180161` |
| Held-out causal loss with only the GQA path | `7.899890` |
| Loss increase when branches were removed | `0.719729` |

These movement, gradient-coverage, and ablation measurements are evidence of
targeted branch training and functional branch use. They do not establish that
the entire inherited checkpoint was independently pretrained.

## ARC-E disclosure

The final ARC-E experiment directly exposed ARC-E test questions and answer
labels during optimization. All resulting ARC-E scores are contaminated,
in-sample measurements. They are not clean and must not be used as leaderboard-
comparable ARC-E results.

## Claims supported by the evidence

The available evidence supports the following description:

> Quasar-230K is a Ling-initialized sparse-MoE checkpoint with Quasar hybrid-
> attention architecture, materially modified experts, trained Quasar-specific
> branches, and fully disclosed function-preserving coordinate conversions.

The evidence does **not** establish from-scratch pretraining, independent model
origin, a clean ARC-E score after label exposure, or a historical 230,000-step
claim solely from the stored weights.

## Sealed revision and evidence hashes

The canonical expert-storage revision was produced from parent revision
`8bcf01098510825472aefe7215cd33db808f7825` and published as revision
`383c90e8c214cad159db6c825e5aad62d0911c39` before the repository became
unavailable to the current authenticated API session.

| Artifact | SHA-256 |
|---|---|
| Canonical runtime config | `d77b6ee29bca3f92a47876fafe41e88feae33555d1bdafcbf8d7ca4d9cf9096b` |
| Full transformation provenance | `26c7c41aef131da6d55f15e4534e6eb2f6c30598e032ea50a32f42e89fa93657` |
| Canonicalization report | `833a908f21cfb7f6adf89aa58ad73d391efbd83ca6f6b8be955f5787985d5743` |
| Canonicalization equivalence report | `6bbd89354268da593584871969e7b786ec78eaab2cc3517afe780912f04fec5f` |
| Remote verification report | `7ec94481ae6e634a58f98eca95210202edd59a8fecacb274e1b75aa6123c6471` |

The private weight repository should retain a minimal pointer to this disclosure:

```json
"provenance_url": "https://huggingface.co/silx-ai/Clear-Quasar-230k"
```