ISOM-R1-Enterprise-40B (Upgraded with ISOM-R2 1M Engine)

🚀 ISOM-R2 1,000,000-Token Engine Upgrade

This repository has been upgraded with the ISOM-R2 architecture (ISOMR2VirtualSVDCache + ISOMR2Engine), the same 1M-token streaming-retrieval engine powering Prannesshkva/Aazhi-Coder-1.5B (verified 1,000,000-token benchmark notebook):

• Hierarchical Paged Virtual SVD Cache (ISOMR2VirtualSVDCache): Streams up to 1,048,576 tokens in 2,048-token chunks with DiskBackedPagePool, NeedleVaultBuffer, and $SO(d)$ Lie-algebra manifolds while bounding active GPU KV memory.
• Hybrid Lexical + Dense Micro-Window Retrieval: Automatically intercepts contexts $> 2,048$ tokens inside model.generate() when use_isom_r2_svd=True (enabled by default in config.json), or via ISOMR2Engine in isom_r2_engine.py.

Overview

ISOM-R1-Enterprise-40B-Beta brings the ISOM bounded dynamic KV cache engine (IsomStateCache) and the ISOM-R2 1M Virtual SVD Cache (ISOMR2VirtualSVDCache) to the 40-billion parameter Falcon foundation. Designed for enterprise deliberation and long document analysis, it addresses the memory constraints of deep 60-layer multi-query attention architectures by combining in-flight symmetric INT8 quantization with multi-head landmark Shannon saliency pruning, maintaining a bounded working memory footprint across extended contexts.

Architecture & Weights Notice: ISOM-R1-Enterprise-40B operates as an inference-time architectural execution wrapper. It loads Falcon-40B weights unmodified at runtime; no gradient fine-tuning or weight modifications have been applied to the base model parameters. Full attribution to the Technology Innovation Institute (TII) is maintained in NOTICE in strict accordance with the Apache License, Version 2.0. Novel cache mechanics and manifold operators are authored by Prannessh K.V.A. under CC BY-NC-ND 4.0.

Theoretical Architectural Specifications

Theoretical MQA Tensor Geometry & Memory Bounds

Memory derivation (Falcon-40B MQA): Falcon-40B has 60 layers, 8 KV heads, and head_dim = 128.

• FP16 KV cache: 60 layers × 8 KV-heads × 128 head-dim × T tokens × 2 bytes × 2 (K and V)
• INT8 KV cache (quantized tensors only): 60 × 8 × 128 × T × 1 byte × 2 (K and V)
• INT8 scale tensor overhead: 60 × 8 × T × 2 bytes (float16) × 2 (K and V) — one scale value per token per head per layer.
• INT8 total = quantized tensors + scale tensors

[!NOTE] Architectural Specification Notice: Values above are theoretical dimensional derivations based on Falcon-40B MQA tensor geometry (60 × 8 KV-heads × 128 head-dim). Empirical validation across 32K sequences requires enterprise multi-GPU hardware (24GB–80GB) and is not claimed as an audited hardware measurement. The ISOM column shows the bounded footprint at a 4,096-token budget regardless of actual sequence length. The compression ratio vs. FP16 at 32K is 7.500 GB / 0.473 GB ≈ 15.9× peak, measured at the 32,768-token upper bound only; the ratio at 4,096 tokens is 1.0× (no reduction, sequence fits in budget).

Quickstart & Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prannesshkva/ISOM-R1-Enterprise-40B-Beta"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Analyze the stability of geodesic flows on compact Lie groups under perturbed curvature tensors."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation & Licensing

@article{prannessh2026isom40b,
  title={ISOM-R1-Enterprise-40B: Bounded-Memory Multi-Query Attention at 40B Scale},
  author={Prannessh K.V.A.},
  journal={CERN Zenodo},
  year={2026},
  doi={10.5281/zenodo.22649142},
  url={https://doi.org/10.5281/zenodo.22649142}
}

• Sole Author & Architect: Prannessh K.V.A.
• LinkedIn: Prannessh K.V.A.
• License: Released under CC BY-NC-ND 4.0 / BSL 1.1 for research and evaluation. Commercial production licenses available via LinkedIn inquiry.

Notice of Non-Endorsement & Independent Lineage

[!IMPORTANT] Independent Derivative Work: ISOM-R1-Enterprise-40B is an independent research implementation engineered solely by Prannessh K.V.A. (Author & Architect). It builds upon tiiuae/falcon-40b-instruct under the Apache 2.0 License. This release is not affiliated with, endorsed by, or sponsored by the Technology Innovation Institute (TII). All continuous isometric state operator manifolds, Cayley SO(d) projection operators, and memory-bounding integrations are proprietary research contributions of the author.