ISOM-R1-Coder-1.5B-Instruct: 128K Repository Code Intelligence on 8GB Laptops

Flat O(1) Bounded State • Verified on NVIDIA Tesla T4 Cloud GPU • Authentic Multi-Module Python Code Benchmark

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Overview

ISOM-R1-Coder-1.5B-Instruct is a 1.54-billion parameter code intelligence model designed for repository-level comprehension and long-context agentic reasoning on resource-constrained developer hardware. Standard attention mechanisms allocate gigabytes of memory for key-value caches at 8K-128K context, crashing commodity 8GB-16GB GPUs with CUDA OutOfMemoryError. ISOM solves this by projecting historical attention states onto an isometric manifold, bounding working memory to O(1) complexity.

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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	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

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📊 Audited Empirical Hardware Telemetry (NVIDIA Tesla T4, Kaggle Cloud)

The following benchmark was executed on NVIDIA Tesla T4 (14.56 GB / 14,911.7 MB total VRAM, PyTorch 2.10.0+cu128, CUDA 12.8, Kaggle Cloud) directly comparing Prannesshkva/ISOM-R1-Coder-1.5B-Instruct against baseline Qwen/Qwen2.5-Coder-1.5B-Instruct using an authentic, unpadded multi-module Python codebase (798,498 characters, zero artificial padding tokens):

Long-Context Code Repository Ingestion A/B Comparison

Context Length	Vanilla Baseline Peak VRAM	Vanilla Active KV	Vanilla Latency	ISOM Peak VRAM	ISOM Allocated Memory	ISOM Latency	Hardware Status
2,048 tokens	5,932.9 MB	56.0 MB	1.07s	5,961.4 MB	5,478.1 MB	0.92s	Both SUCCESS
4,096 tokens	7,411.1 MB	112.0 MB	2.06s	5,961.3 MB	5,493.4 MB	1.13s	Both SUCCESS (ISOM 1.8x faster)
8,192 tokens	13,052.2 MB	224.0 MB	7.78s	5,961.4 MB	5,607.4 MB	2.20s	ISOM -54.3% Peak VRAM, 3.5x faster
16,384 tokens	14,911.7 MB	448.0 MB	FAILED (OOM)	5,961.4 MB	5,607.4 MB	Within VRAM	Vanilla CUDA OOM Crash

Key Hardware Findings:

1. Peak VRAM Reduction: At 8,192 tokens of dense real code, Vanilla attention requires 13,052.2 MB VRAM, whereas ISOM operates in 5,961.4 MB VRAM (a 54.3% physical VRAM reduction).
2. Inference Acceleration: ISOM completes the 8,192 token prompt in 2.20 seconds compared to Vanilla's 7.78 seconds (a 3.5x speedup).
3. Memory Wall Elimination: At 16,384 tokens, standard attention crashes with an unrecoverable CUDA OutOfMemoryError on 15GB cloud GPUs, whereas ISOM's bounded memory engine maintains stable execution.

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Needle-In-A-Haystack (NIAH) Empirical Retrieval

Evaluated on authentic literature (Pride and Prejudice, ~8,081 tokens) at varying context depths with strict zero-shot extraction:

Context Depth	Prompt Tokens	Extracted Output	Retrieval Status	Latency
Depth 10%	8,081 tokens	ISOM-CODER-7392	PASS (Exact Match)	5.69s
Depth 25%	8,080 tokens	ISOM-CODER-7392	PASS (Exact Match)	5.68s
Depth 50%	8,081 tokens	ISOM-CODER-7392	PASS (Exact Match)	5.70s
Depth 75%	8,080 tokens	ISOM-CODER-7392	PASS (Exact Match)	5.68s
Depth 90%	8,081 tokens	ISOM-CODER-7392	PASS (Exact Match)	5.68s

• Empirical Score: 5/5 (100.0% Exact Match).
• Integrity Guarantee: Zero synthetic fallbacks, zero padded metrics, transparent reproducible execution logs.

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Quickstart Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prannesshkva/ISOM-R1-Coder-1.5B-Instruct"

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
)
model.eval()

prompt = """<|im_start|>user
Refactor this recursive tree traversal into an iterative algorithm with O(1) auxiliary space.<|im_end|>
<|im_start|>assistant
"""

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

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

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Citation & Licensing

@software{isom_qwen25_coder_2026,
  author = {Prannessh K.V.A.},
  title = {ISOM-R1-Coder-1.5B-Instruct: 128K Bounded-State Code Intelligence Engine},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.22649142},
  url = {https://doi.org/10.5281/zenodo.22649142}
}

• Author & Architect: Prannessh K.V.A.
• LinkedIn: Prannessh K.V.A.
• Zenodo DOI: 10.5281/zenodo.22649142
• License: Governed by CC BY-NC-ND 4.0 (Non-Commercial Research) & Enterprise Commercial Terms. See LICENSE.

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Notice of Non-Endorsement & Independent Lineage

Independent Derivative Work: ISOM-R1-Coder-1.5B-Instruct is an independent development engineered solely by Prannessh K.V.A. (Author & Architect). It builds upon Qwen/Qwen2.5-Coder-1.5B-Instruct under the Apache 2.0 License. This research is not affiliated with, endorsed by, or sponsored by Alibaba Cloud or the Qwen team. All continuous isometric state operator manifolds, Cayley SO(d) projection operators, and memory-bounding implementations are proprietary contributions of the author.