🧪 DGPL Experimental 1 (dgpl-experimental-1)

1.12B Total Capacity · 144.71M Active Parameters per Token · Native 1-Million Token Context · Single-Model LoopSpec Speculative Edge Acceleration

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⚠️ EXPERIMENTAL RESEARCH PROTOTYPE · NOT FOR PRODUCTION CRITICAL USE

dgpl-experimental-1 is an early-stage experimental foundation research prototype developed by Durbhasi Gurukulam Private Limited (DGPL) to explore the frontiers of ultra-lightweight edge Mixture-of-Experts (MoE), recurrent looped reasoning (16 physical $\to$ 48 effective layers), and single-model LoopSpec self-speculative decoding on consumer edge hardware.

Known Experimental Boundaries & Research Observations:

1. Strengths: High execution throughput (61–85 tok/s on NVIDIA RTX 2050 4GB / >220 tok/s CPU), strong general logical deduction, basic arithmetic, physics derivations, and non-blocking Linux administration.
2. Boundary Limitations: In complex non-homogeneous differential equations with resonance (e.g. $y'' - 4y' + 13y = e^{2x}\cos 3x$), the prototype may omit exponential multipliers in trial ansatzes ($y_p$) or enter repetitive loops without sampling penalties.
3. Sampling Hardening Requirement: To avoid n-gram repetition loops during long reasoning sequences, inference engines MUST enforce non-zero penalties:
   repeat_penalty: 1.25, presence_penalty: 0.5, frequency_penalty: 0.4, repeat_last_n: 256.

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🏛️ Model Architecture Specifications

┌────────────────────────────────────────────────────────────────────────────────────────┐
│                        DGPL EXPERIMENTAL 1 ARCHITECTURE SUMMARY                        │
├───────────────────────────────┬────────────────────────────────────────────────────────┤
│ Model Identifier              │ dgpl-experimental-1                                    │
│ Total Parameter Capacity      │ 1,123,553,280 Parameters (1.12 Billion)                │
│ Active Compute per Token      │ 144,711,680 Parameters (144.71M Active Compute)        │
│ MoE Topology                  │ 1 Shared Expert + 15 Routed SwiGLU Experts (Top-2)     │
│ Physical Layers               │ 16 Transformer Layers                                  │
│ Effective Recurrent Depth     │ Up to 48 Effective Reasoning Passes (K=3 Looped)       │
│ Attention Mechanism           │ 14 Sliding Window (W=1024) + 2 Differential Attention   │
│ Differential Attention Math   │ Softmax(Q1 K1^T / sqrt(d)) - lambda * Softmax(Q2 K2^T) │
│ Native Context Window         │ 1,048,576 Tokens (1M Tokens Default)                   │
│ 1M Context KV RAM Footprint   │ < 272 MB RAM (Clamped Sliding Window Ring Buffer)      │
│ Multi-Token Prediction (MTP)  │ k = 1 Single-Model LoopSpec Speculative Decoding       │
│ Empirical GPU Speed           │ 61.6 – 85.4 tok/s (NVIDIA GeForce RTX 2050 4GB)       │
└───────────────────────────────┴────────────────────────────────────────────────────────┘

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📦 5-Tier GGUF Quantization Matrix

GGUF Artifact	Quantization Format	File Size	Recommended Deployment Target
dgpl_experimental_1_f16.gguf	Full Precision FP16	1,080.50 MB	High-VRAM GPU Serving & Baseline Research
dgpl_experimental_1_q8_0.gguf	8-Bit Quantized	572.30 MB	High-Fidelity Desktop & Server Serving
dgpl_experimental_1_q6_k.gguf	6-Bit K-Quant	428.15 MB	Balanced Precision for Standard 4GB–6GB GPUs
dgpl_experimental_1_q5_k_m.gguf	5-Bit Medium	374.80 MB	4GB GPUs & Compact Laptop Accelerators
dgpl_experimental_1_q4_k_m.gguf	4-Bit Edge Medium	312.45 MB	<350 MB VRAM · NVIDIA RTX 2050 / 3050 & Edge Devices

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🚀 Quickstart: Local Serving via Ollama

1. Create Modelfile

FROM ./dgpl_experimental_1_q4_k_m.gguf

TEMPLATE """<|im_start|>system
You are an expert reasoning engine. Solve the given problem step-by-step.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
<think>
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "</think>"
PARAMETER temperature 0.2
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.25
PARAMETER presence_penalty 0.5
PARAMETER frequency_penalty 0.4
PARAMETER repeat_last_n 256
PARAMETER num_ctx 32768
PARAMETER num_predict 4096
PARAMETER num_gpu 99

2. Build and Run

ollama create dgpl-experimental-1:1.1b -f Modelfile
ollama run dgpl-experimental-1:1.1b "Calculate 47 * 89 step by step."

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🏢 Sovereign Corporate Attribution

• Entity Legal Name: Durbhasi Gurukulam Private Limited (DGPL)
• Registered Office: 2., P. NO. 18, Radha Vihar, Manchwa, Jaipur, Rajasthan 303706, Bharat
• Official Support: support@durbhasigurukulam.com | +91 7852034945
• Governance: Sovereign DGPL Edge AI Research Infrastructure

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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
© 2026 Durbhasi Gurukulam Private Limited (DGPL). All rights reserved.