Ael-Edge-130M-MoE

Prannesshkva/Ael-Edge-130M-MoE

Ael-Edge-130M-MoE is a text generation model from Prannesshkva. Use it when you need the model to write or continue text. The card lists the license as cc-by-nc-nd-4.0.

<p align="center" <a href="https://doi.org/10.5281/zenodo.22649142"<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.22649142.svg" alt="DOI"</a <a href="https://www.linkedin.com/in/prannesshkva/"<img src="https://…

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At a glance

Task: Text Generation
License: cc-by-nc-nd-4.0
Model type: isom_130m_moe
Access: Public
Created: Aug 30, 2026
Updated: Oct 5, 2026
SHA: 84c6e692

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Model card

From the Hugging Face model README

⚡ Ael-Edge-130M-MoE (Powered by the ISOM-R2 1M Engine)

0.0469 MB Invariant Recurrent State • ISOM-R2 1,000,000-Token Retrieval Engine • Verified on NVIDIA Tesla T4

🚀 Ael-Edge-130M-MoE & The ISOM-R2 1,000,000-Token Engine

Ael-Edge-130M-MoE (formerly ISOM-R1-Edge-130M-MoE-Beta-Prototype) is the ultra-compact 134.89M-parameter (58.27M active per token) continuous recurrent state-space + Mixture-of-Experts model in the Ael Model Family, powered internally by the ISOM-R2 1,048,576-token architecture (ISOMR2VirtualSVDCache + ISOMR2Engine):

Combines Ael-Edge-130M-MoE's 0.0469 MB continuous quasi-unitary SSM recurrence with ISOM-R2's Hierarchical Paged Virtual SVD Cache (ISOMR2VirtualSVDCache) and Hybrid Lexical + Dense Micro-Window Retrieval.
Automatically activates chunked streaming + salient micro-window retrieval inside model.generate() for contexts $> 2,048$ tokens (use_isom_r2_svd=True enabled by default in config.json).

🌐 The Ael Model Family (Powered by ISOM-R2)

Model Repository	Architecture Class	Base Lineage	Parameters (Total / Active)	Verified 1M Context (Colab A100)	Peak VRAM (Colab A100)	1M Prefill + Decode Time	Exact Output Match
Prannesshkva/Ael-Coder-1.5B	`AelCoder15BForCausalLM`	Qwen2.5-Coder-1.5B-Instruct	1.54B Dense	1,052,188 tokens (514 chunks)	3.51 GB (Weights: 3.09 GB)	6.10 s (172,490 tok/s)	`CaptureStd` (100%)
Prannesshkva/Ael-Reasoning-1.5B-Instruct	`AelReasoning15BForCausalLM`	Qwen2.5-1.5B-Instruct	1.54B Dense	1,052,188 tokens (514 chunks)	3.98 GB (Weights: 3.56 GB)	6.07 s (173,342 tok/s)	`CaptureStd` (100%)
Prannesshkva/Ael-Coder-16B-MoE	`AelCoder16BMoEForCausalLM`	DeepSeek-Coder-V2-Lite-Instruct	15.71B / 2.36B Active	1,089,849 tokens (533 chunks)	32.06 GB (Weights: 31.49 GB)	15.86 s (68,717 tok/s)	`CaptureStd` (100%)
Prannesshkva/Ael-Enterprise-40B	`AelEnterprise40BForCausalLM`	Falcon-40B (4-bit NF4)	40.0B Dense	1,056,780 tokens (516 chunks)	22.84 GB (Weights: 21.71 GB)	15.39 s (68,670 tok/s)	`CaptureStd)` (100%)
Prannesshkva/Ael-Edge-130M-MoE	`AelEdge130MMoEForCausalLM`	Continuous SSM + 8-Expert MoE	134.89M / 58.27M Active	Unbounded / 1M R2	0.0469 MB Recurrent State	21,049 tok/s (Tesla T4)	Verified Drafter
Prannesshkva/Ael-504M	`Ael504M`	Compact Foundation Model	504M	Compact Edge	Ultra-Light	Sub-second	Verified

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

Evaluated on an NVIDIA Tesla T4 (14.56 GB VRAM, PyTorch 2.10.0+cu128, CUDA 12.8) across an unpadded continuous literature stream (Pride and Prejudice, 728,846 characters) from 10,000 up to 100,000 continuous tokens:

Continuous Stream Length	Active Recurrent State	Allocated GPU Memory	Peak GPU VRAM	Generation Throughput	Spatial Complexity Profile	Hardware Status
10,000 tokens	0.0469 MB	1,176.9 MB	2,631.8 MB	8,645.2 tok/s	Constant O(1) (< 1 MB)	SUCCESS
25,000 tokens	0.0469 MB	1,656.4 MB	4,795.1 MB	14,476.6 tok/s	Constant O(1) (< 1 MB)	SUCCESS
50,000 tokens	0.0469 MB	2,614.9 MB	7,686.7 MB	17,844.6 tok/s	Constant O(1) (< 1 MB)	SUCCESS
75,000 tokens	0.0469 MB	2,614.9 MB	8,645.2 MB	19,885.7 tok/s	Constant O(1) (< 1 MB)	SUCCESS
100,000 tokens	0.0469 MB	2,614.9 MB	8,645.2 MB	21,049.0 tok/s	Constant O(1) (< 1 MB)	SUCCESS

Model Specifications

Parameter	Value
Model Class	`AelEdge130MMoEForCausalLM` (`AelEdge130MMoEConfig`)
Total Parameters	134.89M Untied (109.16M Tied Backbone) / 58.27M Active per token
Architecture	Continuous Isometric SSM + Mixture-of-Experts + ISOM-R2 1M Engine
Layers / Hidden Dim / State Dim	6 Layers / `d_model = 512` / `d_state = 8`
Experts	8 SwiGLU Experts per layer (TopK=2)
Recurrent Working State	0.0469 MB ($O(1)$ constant footprint: 6 layers × 512 channels × 8 states × 2 bytes bf16)

🚀 Quickstart Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Prannesshkva/Ael-Edge-130M-MoE"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
).eval()

prompt = "Solve step by step: If a car travels 90 km/h for 3.5 hours, what is the total distance traveled?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7,
        do_sample=True,
        tokenizer=tokenizer,
    )

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

Citation & Licensing

@software{ael_edge_130m_2026,
  author = {Prannessh K.V.A.},
  title = {Ael-Edge-130M-MoE: Continuous Recurrent SSM + Mixture-of-Experts Powered by ISOM-R2},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.22649142},
  url = {https://huggingface.co/Prannesshkva/Ael-Edge-130M-MoE}
}

Sole Author & Architect: Prannessh K.V.A. (LinkedIn)
License: Governed by CC BY-NC-ND 4.0 (Non-Commercial Research) & Enterprise Commercial Terms. See LICENSE.

Task: Text Generation
Type: isom_130m_moe
License: cc-by-nc-nd-4.0
Languages: en
Created: Aug 30, 2026
Updated: Oct 5, 2026