ISOM-R1-Enterprise-40B: 40B System-2 Deliberation Foundation
Overview
ISOM-R1-Enterprise-40B brings bounded-state memory and continuous isometric operator manifolds to 40-billion parameter foundation scale. Designed for enterprise deliberation and long document analysis, it addresses the memory constraints of deep 60-layer multi-query attention architectures.
| 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 | 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 | 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 | 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 |
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) |
Theoretical MQA Tensor Geometry & Memory Bounds
In standard Multi-Query Attention (MQA) across 60 layers:
| Context Length | Standard MQA Attention KV (FP16) | Standard MQA Attention KV (INT8) | ISOM Theoretical State Spec (INT8) |
|---|---|---|---|
| 4,096 tokens | 0.49 GB | 0.25 GB | 0.25 GB |
| 8,192 tokens | 0.98 GB | 0.49 GB | 0.49 GB |
| 16,384 tokens | 1.97 GB | 0.98 GB | 0.49 GB |
| 32,768 tokens | 3.93 GB | 1.97 GB | 0.49 GB |
[!NOTE] Architectural Specification Notice: Values above represent theoretical dimensional derivations based on Falcon-40B Multi-Query Attention (MQA) tensor geometries. Empirical validation across 32K sequences requires enterprise multi-GPU hardware (24GB-80GB) and is not claimed as an audited hardware measurement.
Quickstart & Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Prannesshkva/ISOM-R1-Enterprise-40B"
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-40Bis an independent research implementation engineered solely by Prannessh K.V.A. (Author & Architect). It builds upontiiuae/falcon-40b-instructunder 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.