ISOM-R1-Coder-16B-MoE-Beta (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 across DeepSeek-Coder-V2 Multi-Head Latent Attention (MLA) layers using DiskBackedPagePool, NeedleVaultBuffer, and $SO(d)$ Lie-algebra manifolds.
• Hybrid Lexical + Dense Micro-Window Retrieval: Automatically intercepts repository 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-Coder-16B-MoE-Beta is a 16-billion parameter Mixture-of-Experts (MoE) reasoning and code intelligence model engineered for constant-memory execution over massive repository contexts. Powered by the ISOM-R1 / ISOM-R2 bounded manifold architecture, it incorporates 64 routed experts (2 shared, TopK=6 activated per token) and Multi-Head Latent Attention (MLA) wrapped in a native continuous state-space and virtual SVD caching engine.

Traditional Transformers suffer from severe $O(N)$ linear memory explosion in the KV cache across long sequences. By dynamically projecting Multi-Head Latent Attention states onto a continuous isometric manifold (Float64 Cayley SO(d) with multi-head landmark Shannon saliency and hybrid lexical/dense micro-window retrieval), the ISOM-R2 engine guarantees an $O(1)$ bounded working memory footprint across 160,000 to 1,000,000+ token traces.

The ISOM Model Family

Architectural Specifications

Mathematical Foundation: The 160K Memory Wall

In standard Multi-Head Latent Attention without ISOM, cache memory grows linearly with context length:

$$M_{\text{vanilla}}(N) = 2 \times L \times H \times d_v \times N \times \text{bytes}$$

At 160K context (163,840 tokens) across 27 layers with 16 attention heads and $d_v = 128$: $$M_{\text{vanilla}}(160\text{K}) = 2 \times 27 \times 16 \times 128 \times 163,840 \times 2 \approx \mathbf{36.21\text{ GB}}$$

Under the ISOM architecture, historical key-value states exceeding the reasoning threshold $B = 16,384$ are projected onto the isometric manifold:

$$M_{\text{ISOM}}(N) = \min(N, B) \times \text{stride} + \text{dim}(\mathcal{M}_{\text{Cayley}})$$

[!NOTE] Architectural Specification Notice: Values above represent theoretical dimensional derivations based on Multi-Head Latent Attention (MLA) parameters. Empirical validation across 160K sequences requires an 80GB enterprise GPU cluster and is not claimed as an audited hardware measurement.

Quickstart & Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Prannesshkva/ISOM-R1-Coder-16B-MoE-Beta"

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Load Model with native ISOM execution
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = """<|im_start|>user
Write a high-performance concurrent queue in Python using lock-free atomic CAS operations.<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.3,
        do_sample=True
    )

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

Citation & Contact

@software{isom_coder_16b_beta_2026,
  author = {Prannessh K.V.A.},
  title = {ISOM-R1-Coder-16B-MoE-Beta: 160K Repository-Scale Bounded-Memory MoE},
  year = {2026},
  publisher = {Zenodo},
  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.
• Zenodo DOI: 10.5281/zenodo.22649142
• Space: ISOM-Benchmark

Notice of Non-Endorsement & Independent Lineage

[!IMPORTANT] Independent Derivative Work: ISOM-R1-Coder-16B-MoE-Beta is an independent research implementation developed solely by Prannessh K.V.A. (Author, Architect & IP Holder). It utilizes the foundation weight initialization and base MoE topology of deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct under the official DeepSeek Model License. This release is not endorsed by, sponsored by, or affiliated with DeepSeek AI or its parent entities. All modifications, continuous Cayley SO(d) projection operators, dynamic first-layer detection, and isometric memory-bounding manifolds are original contributions of the author.