---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
  - igbundle
  - hyperbolic-geometry
  - adapter
  - research-artifact
  - negative-result
library_name: pytorch
---

# ManifoldGL cp3000 — IGBundle Geometric Adapter (Honest Artifact)

**This is a research artifact accompanying a falsification study, not a
performance checkpoint.** It is published for reproducibility of the analyses
in `draft_paper_falsification.md` (IGBundle-LLM repository), including the
curvature-telemetry weight-invariance result (§2.6).

## What this is

A raw PyTorch state dict (`adapter_weights.pt`, pickle format, 58,705,739
bytes) for the **GeometricIGBundleAdapter** — a nonlinear residual adapter
module injected into transformer blocks of a frozen Qwen2.5-7B. It is **not** a
PEFT/LoRA adapter and cannot be loaded with `PeftModel.from_pretrained`.

## Integrity & provenance

| Field | Value |
|---|---|
| SHA256 (`adapter_weights.pt`) | `2004373636B049FC03771EE087FF1E4053D8003C18C8F3FA94668D598145DB14` |
| Training script | `train_refined_hf.py` (IGBundle-LLM repo) |
| Training step | 3000 (of a 5000-step Phase 8 run) |
| Base model | Qwen2.5-7B (local cp600 merge lineage), 4-bit NF4 during training |
| Code commit required to instantiate | `579dd7c` (branch `dev-multimodal-physics`, includes the 2026-07 audit fixes; any commit ≥ `7522e99` instantiates the architecture) — the public GitHub snapshot from January 2026 **cannot** instantiate this checkpoint (missing `dynamics/`, `geometry/poincare.py`, geodesic attention) |

## Configuration (exact, from the training script)

```python
from igbundle.core.config import IGBundleConfig

config = IGBundleConfig(
    hidden_size=3584,          # Qwen2.5-7B
    latent_dim=64,
    num_components=8,
    num_categories=16,
    use_dynamics=True,
    use_geodesic_attn=True,
    supported_modalities=["vision", "text"],
)
```

## How to load

```python
import sys, torch
sys.path.insert(0, "IGBundle-LLM/src")  # commit 7522e99+

from transformers import AutoModelForCausalLM
from igbundle.core.config import IGBundleConfig
from igbundle.modules.geometric_adapter import create_geometric_adapter
from igbundle.integrations.hf_patch import wrap_hf_candidate

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", torch_dtype="bfloat16")
config = IGBundleConfig(hidden_size=3584, latent_dim=64, num_components=8,
                        num_categories=16, use_dynamics=True, use_geodesic_attn=True)
model = wrap_hf_candidate(model, config)  # injects adapters into every block
state = torch.load("adapter_weights.pt", map_location="cpu", weights_only=True)
# state keys are namespaced per wrapped layer; align with your wrapping depth.
```

## Claim tiers — what this checkpoint does and does not evidence

| Claim | Status |
|---|---|
| Fixed Poincaré kernels (Möbius add, exp/log maps, distance) mathematically correct | **EMPIRICAL** — Ganea formulas verified; 100% kernel-faithfulness tests |
| Learned metric (`metric_chol`) converged to identity (flat) | **EMPIRICAL** — eigenspectrum ≈ 1; verify with `scripts/kappa_invariance_test.py` |
| Published curvature telemetry (K = −5.63/−5.72) reflects learned hyperbolicity | **FALSIFIED** — the estimator is weight-invariant with the hardcoded conformal factor; those values are an architecture constant read at the projection boundary (paper §2.6) |
| Downstream task improvement over base Qwen2.5-7B | **NONE MEASURED** — ARC-Challenge 54.86%, identical to base; ARC eval accuracy 0.01 (n=100) |
| `potential_net` (Hamiltonian dynamics) received training signal | **NO** — gradient path severed by `no_grad`/`detach` in the training-era code (fixed post-hoc behind `config.differentiable_dynamics`) |

## Reproduce the weight-invariance result

```bash
python scripts/kappa_invariance_test.py --checkpoint path/to/adapter_weights.pt
# Verified output (2026-07-05, CPU, float64):
#   trained (cp3000) = identity = analytic closed form = -32.660
#   trained vs identity relative deviation: 1.22e-12  -> WEIGHT-INVARIANT
#   ||trained_chol - I|| / ||I|| = 0.079               -> metric converged to flat
#   radius sweep: kappa -130.8 (r=0.1) ... -4.4 (r=0.9) -> published values sit on
#   the fixed curve near the r=0.95 projection boundary
```

## Retractions

Figures previously associated with this line of work — 28.7% vs 12.4%
accuracy, "MFR 94.2%", "+131.5% ARC improvement", "K = −5.63 strongly
hyperbolic" — are retracted; they are refuted by the project's own evaluation
artifacts. See `draft_paper_falsification.md` §2.5–§2.7 for the full
reconciliation and the measured values.