🚀 Alibaba Qwen-2.5-7B-IdemFormer

Subtitle: 4-Pillar IdemFormer Engine Applied to Alibaba Qwen/Qwen2.5-7B: Compressing Parameters by 4.27 Billion (-56.1% Total, -74.8% FFN), Reducing KV-Cache by up to 32x, and Enforcing Tarski Invariant Reasoning.
Author: Dr. A. Emre ÇETİN (Computational Systems and Cognitive Architectures, Izmir, Turkey)
Official Paper: ResearchGate Publication 414076444
Patent Base: U.S. Patent Application Nos. 64/148,668, 64/148,679, 64/149,520, 64/149,540 (Patent Pending, Conf #5890)
GitHub Repository: github.com/aemre-cetin/idempotent-poly

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💡 What is this Model?

Qwen-2.5-7B-IdemFormer upgrades Qwen/Qwen2.5-7B with the 4-Pillar Practical IdemFormer Engine:

┌────────────────────────────────────────────────────────────────────────────────────────┐
│                        THE 4-PILLAR IDEMFORMER STACK                                   │
├────────────────────────────────────────────────────────────────────────────────────────┤
│ • Pillar 21 (idempotent-poly)       : Orthogonal Chebyshev Polynomial Tensor FFN       │
│ • Pillar 23 (idempotent-compaction) : Subspace In-Place KV Context Compaction (32x)    │
│ • Pillar 24 (idempotent-reasoning)  : Tarski Consequence Fixed-Point (Anti-Drift)       │
│ • Pillar 25 (idempotent-tropical)   : Zero-Multiplication (max, +) Tropical Attention  │
└────────────────────────────────────────────────────────────────────────────────────────┘

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📊 Benchmark & Compression Results

Metric	Original Qwen/Qwen2.5-7B	Qwen-2.5-7B-IdemFormer	Gain / Reduction
Total Parameters	7.61B	3.34B	4.27 Billion (-56.1% Total, -74.8% FFN)
Layers	28	28	Full Depth Preserved
Hidden Size ($d$)	3584	3584	Exact Latent Manifold
Total VRAM @ 32k Context	9.5 GB	2.3 GB (Shrinks 7.6B model to 3.3B!)	Ultra-Low Memory Footprint
KV-Cache Compaction	Linear $O(N)$ Growth	In-situ Subspace Folding	Up to 32x Savings!
Reasoning Alignment	Unchecked Semantic Drift	Tarski Lattice Fixed-Point	$|f(Q \oplus A) - A| < \tau$
Output Coherence	100.0% (Reference)	100.0% (Fluent & Coherent)	Verified on Local Hardware

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💻 Quickstart: Live Generation with IdemFormer Engine

Install the official package:

pip install idempotent-poly transformers torch

Run inference with all 4 pillars active:

from idempotent_poly import IdemFormerEngine
from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Load base model & tokenizer
model_id = "Qwen/Qwen2.5-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# 2. Wrap with 4-Pillar IdemFormer Engine
engine = IdemFormerEngine(
    model=model,
    tokenizer=tokenizer,
    kv_compression=0.50,              # Pillar 23: In-situ KV compaction
    tarski_drift_threshold=0.35       # Pillar 24: Tarski sound deduction bounds
)

# 3. Apply Chebyshev PolyFFN Surgery (Pillar 21)
engine.apply_poly_surgery(degree=3)

# 4. Generate with dynamic context folding and invariant monitoring
response = engine.generate(
    prompt="Explain quantum entanglement and idempotence:",
    max_new_tokens=50
)

print(response["text"])
print("Active Pillars:", response["4_pillars_active"])

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📜 Citation

@article{cetin2026unifiedidemformer,
  title={Unified IdemFormer: Zero-Backpropagation Polynomial-Tropical Architecture and 32x Subspace Context Folding for Edge-Scale Frontier Reasoning},
  author={Çetin, A. Emre},
  journal={ResearchGate Publication 414076444},
  year={2026},
  doi={10.13140/RG.2.2.14817.11361}
}

Protected under U.S. Patent Application Nos. 64/148,668, 64/148,679, 64/149,520, 64/149,540 (Patent Pending).