Prannesshkva/ISOM-R1-Enterprise-40B-Beta

ISOM-R1-Enterprise-40B: 40B System-2 Deliberation Foundation

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Overview

ISOM-R1-Enterprise-40B-Beta brings the ISOM bounded dynamic KV cache engine (IsomStateCache) 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.

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Model	Primary Architecture Role	Base Lineage (Independent Derivative)	Total / Active Parameters	Max Context	Cache Complexity	Hardware Target
ISOM-R1-Coder-16B-MoE	160K Bounded Code & MLA MoE	DeepSeek-Coder-V2-Lite (Non-Endorsed)	15.71B / 2.36B Active	163,840 (160K)	O(1) Bounded Manifold (Architectural Spec)	16GB Cloud / Multi-GPU
ISOM-R1-Enterprise-40B-Beta	40B System-2 Foundation Reasoning	Falcon-40B (Non-Endorsed)	40.0B Dense	32,768 (32K)	O(1) Bounded State (Architectural Spec)	Enterprise Multi-GPU (24GB-80GB)
ISOM-R1-Coder-1.5B-Instruct	128K Repository Code Intelligence	Qwen2.5-Coder-1.5B-Instruct (Non-Endorsed)	1.54B Dense	131,072 (128K)	O(1) Bounded State (Tesla T4 Verified)	8GB Developer Laptops / Edge
ISOM-R1-Reasoning-1.5B-Instruct-Beta	32K System-2 Mathematical Deliberation	Qwen2.5-1.5B-Instruct (Non-Endorsed)	1.54B Dense	32,768 (32K)	O(1) Bounded State (Tesla T4 Verified)	8GB Edge / Consumer GPUs
ISOM-R1-Edge-130M-MoE-Beta-Prototype	Unbounded Recurrent Drafter & SSM	Standalone Continuous SSM + MoE	134.89M / 58.27M Active	Unbounded Recurrence	O(1) Recurrent State (0.0469 MB Verified)	Ultra-Low Power Edge & CPU

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Theoretical Architectural Specifications

Metric	Specification
Total Parameters	40.0 Billion Dense
Layers	60 Deep Decoder Layers
Attention Architecture	Multi-Query Attention (MQA, 8 KV heads)
Base Model	tiiuae/falcon-40b-instruct (Non-Endorsed)
Context Window	32,768 tokens (32K)
Working Memory Complexity	O(1) Bounded State (Architectural Spec)
Target Hardware	Enterprise Multi-GPU (24GB-80GB)

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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

Context Length	Standard MQA KV (FP16)	Standard MQA KV (INT8 tensors only)	INT8 Scale Overhead	INT8 Total	ISOM Bounded State (budget=4096, INT8 total)
4,096 tokens	0.938 GB	0.469 GB	0.004 GB	0.473 GB	0.473 GB
8,192 tokens	1.875 GB	0.938 GB	0.008 GB	0.946 GB	0.473 GB (capped at 4K budget)
16,384 tokens	3.750 GB	1.875 GB	0.015 GB	1.890 GB	0.473 GB (capped at 4K budget)
32,768 tokens	7.500 GB	3.750 GB	0.030 GB	3.780 GB	0.473 GB (capped at 4K budget)

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).

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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))

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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.

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Notice of Non-Endorsement & Independent Lineage

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.