ISOM-R1-Coder-16B-MoE: 160K Repository-Scale Code & Deliberative MoE

Overview

ISOM-R1-Coder-16B-MoE is a 16-billion parameter Mixture-of-Experts (MoE) code generation model featuring 64 routed experts (2 shared, TopK=6 activated per token) and Multi-Head Latent Attention (MLA). Built upon the DeepSeek-Coder-V2-Lite architecture, it is designed for bounded-state execution over massive 160K repository contexts.

Traditional Transformers suffer from quadratic memory scaling in long contexts. By projecting Multi-Head Latent Attention key-value states onto a continuous isometric manifold (Float64 Cayley SO(d)), ISOM constrains the active working memory footprint over long token sequences.

Theoretical Architectural Specifications

Mathematical Foundation: The 160K Memory Wall

In traditional 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 DeepSeek-Coder-V2-Lite 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"

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

# Load Model
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_2026,
  author = {Prannessh K.V.A.},
  title = {ISOM-R1-Coder-16B-MoE: 160K Repository-Scale Code & Deliberative 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 is an independent research implementation developed solely by Prannessh K.V.A. (Author, Architect & IP Holder). It utilizes the model weights and architecture 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, and isometric memory-bounding manifolds are original contributions of the author.