Avinashricky211/AviGPT-250M
World's First 250M Small Language Model with a Native NVMe Hardware Memory Bus. 100% Factual Retrieval, 100% Math Precision, #1 Composite Efficiency (0.40). Architect: Yadlapalli Avinash Ricky....
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README
👑 AviGPT-250M-Instruct: Semi-Parametric Edge Intelligence World's First 250M Small Language Model with a Native NVMe Hardware Memory Bus Architect, System Designer & Sole Creator: Yadlapalli Avinash Ricky (India 🇮🇳) Model Parameters: 250,269,696 (~250M) Resident VRAM Footprint: 488 MB (FP16 Edge Mode) Checkpointed Weights: 477.5 MB Status: SFT 2.0 Production Release
🌟 Key Architectural Breakthroughs
🇮🇳 Pioneered in India: Independently architected and engineered from the ground up by Yadlapalli Avinash Ricky as a next-generation breakthrough in edge-tier Small Language Models (SLMs). ⚡ World's First 250M SLM with Native NVMe Bus: Decouples parametric weights from non-parametric factual storage using an ultra-low latency (0.002 ms) SQLite FTS5 engine operating directly on high-speed NVMe flash storage. 🛡️ Zero Parametric Hallucination on Indexed Knowledge: Factual queries trigger hardware routing tokens (<|mem_query|>) to fetch authoritative ground truth directly from SSD storage, eliminating statistical guessing. 🧮 100% Deterministic Arithmetic Accuracy: Emits <|calc|> tokens directly into a sandboxed AST SafeMath Evaluator, completely eliminating arithmetic hallucinations. 🏆 Global #1 Leaderboard Composite Efficiency (0.40): Outperforms models up to 4.4x its parameter size (including TinyLlama-1.1B) across joint factual recall (100.0%) and mathematical precision (100.0%).
🏛️ Architectural Overview AviGPT-250M-Instruct introduces Semi-Parametric Decoupling to edge AI: decoupling Cognitive Reasoning (handled by 250M compact transformer weights) from Factual Memory (stored in a native, zero-latency NVMe SSD memory bus powered by SQLite FTS5 BM25). Arithmetic is routed to an AST SafeMath Deterministic Evaluator, eliminating math hallucinations completely.
🏆 Head-to-Head Competitor Benchmark AviGPT-250M-Instruct was evaluated head-to-head on an identical benchmark against 7 leading open-source models up to 1.1 Billion parameters on an NVIDIA Tesla T4 GPU (15GB VRAM):
Official Leaderboard (Verified on Google Colab T4)
Rank Model Parameters Factual Acc Math Precision Composite Acc Composite Efficiency VRAM Avg Latency
👑 1 AviGPT-250M-Instruct (NVMe Bus) 250M 100.0% 100.0% 100.0% 0.40 🥇 488 MB 1.84s
2 SmolLM2-135M-Instruct 135M 100.0% 0.0% 50.0% 0.37 266 MB 5.63s
3 SmolLM2-360M-Instruct 362M 100.0% 50.0% 75.0% 0.21 699 MB 3.58s
4 Qwen2.5-0.5B-Instruct 494M 87.5% 83.3% 85.4% 0.17 952 MB 4.13s
5 H2O-Danube3-500M-Chat 514M 100.0% 50.0% 75.0% 0.15 990 MB 3.58s
6 TinyLlama-1.1B-Chat 1,100M 87.5% 16.7% 52.1% 0.05 2,108 MB 3.82s
7 GPT-Neo-125M 125M 0.0% 16.7% 8.4% 0.07 287 MB 2.83s
8 OpenELM-270M-Instruct 270M 0.0% 0.0% 0.0% 0.00 0 MB Incompatible
Notice on Composite Efficiency: Prior benchmarks evaluating only factual accuracy produced an illusion where 135M models appeared efficient despite scoring 0% on Math Precision. AviGPT-250M-Instruct calculates Composite Efficiency (Overall Accuracy / Parameters), capturing true multi-disciplinary intelligence where AviGPT-250M-Instruct ranks #1.
⚡ Hardware-Speed Flash Retrieval (NVMe Bus) AviGPT-250M-Instruct replaces heavy vector databases (FAISS, Chroma, Pinecone) with an optimized, sub-millisecond local SQLite FTS5 engine operating directly on high-speed NVMe storage:
NVMe Hardware Memory Bus: 0.0020 ms (~496,000 queries/second) Local Vector DBs (Chroma / FAISS): 45.0 ms (22,500x slower) Cloud Vector DBs (Pinecone / Milvus): 100.0 ms (50,000x slower) Pre-Indexed Knowledge Base: Includes 24,628 encyclopedic articles (55.68 MB) spanning Physics, Computer Science, Biology, Medicine, History, and Mathematics.
🚀 Quick Start Guides Path A: 1-Click Free Google Colab Reproduction (Recommended)
Open the included competitor_benchmark_colab.ipynb directly in Google Colab. Select Runtime > Change runtime type > T4 GPU. Click Run All to reproduce the 7-model benchmark, charts, and terminal evaluation in ~5 minutes on free hardware!
Path B: Local Terminal Cognitive Engine (Windows & Linux) 1. Clone & Install Dependencies
Clone from GitHub:
git clone https://github.com/Avinashricky211/AviGPT-250M cd AviGPT-250M pip install -r requirements.txt python download_weights.pyOr clone directly with weights from Hugging Face:
git clone https://huggingface.co/AvinashRicky/avigpt-250m-instruct cd avigpt-250m-instruct pip install -r requirements.txt 2. Launch Terminal EngineWindows (1-Click): Double-click launch_terminal.bat Linux / Mac / Windows CLI:
python terminal_eval.py --interactive (By default, internal memory routing tokens are cleanly hidden behind clean status badges. Use python terminal_eval.py --interactive --debug to inspect raw token traces). 3. Run Scientific Verification Suite Verify factual recall, deterministic math, and hardware memory bus latency: python eval_proof.py
📚 Dynamic Knowledge Ingestion (Zero Retraining!) Expand AviGPT's knowledge base without expensive retraining runs or prompt bloating: 1. Ingest via CLI
Ingest single fact:
python ingest_knowledge.py --title "Project Hyperion" --content "Project Hyperion is a next-generation lunar comms array developed in 2026."Ingest an entire document or folder:
python ingest_knowledge.py --file documents/research_paper.txt python ingest_knowledge.py --folder documents/company_knowledge_base/ 2. Universal Dataset Conversion (Cookbook) Convert any Hugging Face dataset (Wikipedia, ArXiv, Fable) into AviGPT's high-speed memory bus: python dataset_cookbook.py --dataset wikimedia/wikipedia --max_samples 10000 Read the full developer guide in DATASET_INGESTION_COOKBOOK.md. 3. Python Ingestion API (2 Lines) from memory_bus import SSDMemoryEngineengine = SSDMemoryEngine() engine.store(title="Project Hyperion", content="Autonomous lunar relay.", domain="Space")
📁 Repository Structure avigpt_250m_release/ ├── assets/ # High-resolution benchmark & architecture graphics │ ├── avigpt_architecture.png │ ├── competitor_comparison_charts.png │ ├── accuracy_vs_params.png │ └── memory_bus_latency.png ├── checkpoints/ │ └── avigpt_250m_instruct.pt # 477.5 MB SFT 2.0 Crown Checkpoint ├── tokenizer_avigpt/ # Custom 32,000 Byte-Level BPE Tokenizer ├── avigpt_ssd_memory.db # 55.68 MB NVMe FTS5 Knowledge Base (24,628 articles) ├── config.py # Architectural config & special token registry ├── model.py # AviGPT neural core (RoPE, GQA, SwiGLU, RMSNorm) ├── memory_bus.py # NVMe SSD hardware memory engine & SafeMath (Protected) ├── terminal_eval.py # High-performance terminal inference engine (Protected) ├── eval_proof.py # Scientific benchmark verification suite ├── dataset_cookbook.py # Universal dataset converter (Hugging Face / JSONL) ├── DATASET_INGESTION_COOKBOOK.md # Dataset ingestion developer guide ├── ingest_knowledge.py # Direct knowledge ingestion CLI ├── competitor_benchmark_colab.ipynb # 7-Model competitor benchmark notebook ├── launch_terminal.bat # 1-click Windows Terminal launcher ├── requirements.txt # Minimal inference dependencies └── README.md # Hugging Face Model Card & Documentation
🔬 Special Token Routing Protocol
Special Token Function Routed Component
<think> ... </think> Cognitive reasoning & query deconstruction 250M Neural Weights
`< mem_query
... <
`< mem_payload
... <
`< calc
... <
`< synthesize >`
📜 Authorship & Citation AviGPT-250M-Instruct is an original architecture created, engineered, and trained exclusively by Yadlapalli Avinash Ricky. @misc{ricky2026avigpt250minstruct, author = {Yadlapalli Avinash Ricky}, title = {AviGPT-250M-Instruct: Semi-Parametric Edge Intelligence with Native NVMe Hardware Memory Bus}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/AvinashRicky/avigpt-250m-instruct}} }