1. Overview
Arcle V1 is a 5.84-billion-parameter unified omni-multimodal foundation model engineered by Abhinav Anand at ArcleIntelligence. Designed specifically to democratize high-capability AI without cloud subscriptions, Arcle V1 brings conversation, mathematical reasoning, computer vision, document reading, speech recognition, speech synthesis, and video understanding into a single, unified model file.
Unlike pipeline systems that route requests across multiple disparate models behind an orchestration service mesh, Arcle V1 projects all modalities into a shared 2,560-dimensional latent representation space. Every sensory pathway flows through one nn.Module and one unified forward pass.
Key Highlights
- Unified Representation: Text, images, audio, documents, and video project into a shared 2,560-dimensional semantic space.
- Strong Reasoning in Class: Competitive on mathematics, science, and commonsense reasoning benchmarks against leading 3B–4B models (ARC-Easy 80.0%, GSM8K 77.5%, MATH-500 74.2%, HellaSwag 67.0%).
- Hybrid Core Architecture: Powered by a hybrid Mamba-2 State Space Model (SSM) + Causal Self-Attention backbone, delivering linear computational complexity on long sequences while retaining high-precision associative recall.
- Ultra-Long Context: Architectural context window of 2,097,152 tokens (131,072 verified).
- 100% Offline & Private: Zero cloud dependencies or telemetry at inference time. Fully verified with
HF_HUB_OFFLINE=1. - Exact Parameter Budgeting: Exactly 5,840,000,000 parameters solved through precision connector widths and rank allocation.
2. Benchmark Evaluations
Arcle V1 has been evaluated using standardized test harnesses across standard academic and reasoning benchmarks against leading models in the 3B–4B parameter class:
Comparison Against Leading 3B–4B Models
| Benchmark | Arcle V1 (5.84B) | Qwen 3 (4B) | Llama 3.2 (3B-IT) | Apple AFM (3B) | Falcon 3 (3B) | LFM2-2.6B (2.6B) | Gemma 3 (4B) |
|---|---|---|---|---|---|---|---|
| ARC-Easy (Grade-School Science) | 80.0% | 76.8% | 72.5% | 71.0% | 74.2% | 74.6% | 80.0% |
| GSM8K (Math Reasoning) | 77.5% | 80.4% | 77.3% | 75.0% | 74.7% | 74.3% | 38.4% |
| MATH-500 (Complex Math) | 74.2% | 68.5% | 41.2% | 48.0% | 54.2% | 58.4% | 38.6% |
| HellaSwag (Commonsense) | 67.0% | 55.3% | 63.7% | 62.0% | 64.2% | 63.5% | 47.7% |
| BBH (Multi-step Logic) | 53.7% | 51.1% | 41.5% | 44.2% | 45.0% | 44.8% | 50.0% |
| TruthfulQA (Factual Alignment) | 53.2% | 51.8% | 50.3% | 48.5% | 50.1% | 48.2% | 51.7% |
| ARC-Challenge (Hard Science) | 48.5% | 37.7% | 44.9% | 42.5% | 48.2% | 42.1% | 44.9% |
| MMLU (General Knowledge) | 43.5% | 67.0% | 61.8% | 61.4% | 56.8% | 62.5% | 59.5% |
Scores reflect standardized evaluations and published figures from official model cards and technical reports in the same parameter class.
Multimodal Capability Measurements
Verified on official weights across all sensory pathways:
| Modality & Capability | Evaluation Metric | Result | Operational Status |
|---|---|---|---|
| Document OCR (Synthetic) | Content-word recall | 94.6% | Production-ready |
| Document OCR (Scans/PDFs) | Dense multi-column page recall | 49.1% | Functional; single-page focus |
| Speech Recognition (English) | Word Error Rate (WER on FLEURS) | 9.2% | High-fidelity transcription |
| Speech Recognition (Spanish) | Word Error Rate (FLEURS) | 27.6% | Near-verbatim comprehension |
| Image Understanding | Caption overlap & semantic alignment | 61.1% | Visual Q&A and description |
| Visual QA (VQAv2) | Accurate visual question answering | 57.2% | Verified multimodal alignment |
| Video Understanding (MSR-VTT) | Temporal caption overlap | 52.7% | Temporal action & event narration |
| Text-to-Speech (TTS) | Sampling rate / Offline Voice Engine | 24 kHz | Native neural speech generation |
| Parameter Count | Strict count across all shards | 5,840,000,000 | Exact budget verification |
3. Architecture & Parameter Allocation
Arcle V1 is constructed from high-capacity frozen encoders coupled to trained multimodal cross-attention connectors and a high-throughput hybrid reasoning core:
| Subsystem / Layer | Parameters | Status / Weight Nature |
|---|---|---|
| Reasoning & Language Core (Hybrid Mamba-2 SSM + Causal Attention) | 3,289.4M | Trained Parameter |
| Image-Generation Latent Transformer (Linear Attention) | 591.7M | Frozen Foundation |
| Document & Layout Encoder | 491.9M | Frozen Foundation |
| Deep-Compression Autoencoder (32× Compression) | 312.3M | Frozen Foundation |
| Vision Perception Encoder | 316.3M | Frozen Foundation |
| Speech Recognition Encoder | 307.2M | Frozen Foundation |
| Neural Speech Synthesiser | 81.8M | Frozen Foundation |
| High-Capacity LoRA Adapter ($r = 280$) | 213.2M | Trained Parameter |
| Document OCR Semantic Connector | 66.0M | Trained Parameter |
| Image Conditioning Adapter | 65.7M | Trained Parameter |
| Vision Perception Connector | 59.8M | Trained Parameter |
| Temporal Video Processor | 26.5M | Trained Parameter |
| Audio Feature Connector | 15.9M | Trained Parameter |
| TTS Audio Style Connector | 2.2M | Trained Parameter |
| Total Model Weight | 5,840,000,000 | Exact Parameter Budget |
4. Quickstart & Usage
The Arcle V1 repository is completely self-contained. All loaders, inference wrappers, and unified model graphs are included.
Installation
# Clone the repository
git clone https://huggingface.co/Lucifer2006/Arcle-V1
cd Arcle-V1
# Install inference dependencies
pip install -r "Model Inferencing File/requirements.txt"
Interactive CLI Prompt
Start the interactive omni-assistant:
python "Model Inferencing File/model.py"
arcle> Who are you?
arcle> /img photo.jpg What is in this picture?
arcle> /ocr document_scan.png
arcle> /tts Hello from Arcle.
arcle> /help
Python API Integration
import sys
sys.path.insert(0, "Model Inferencing File")
from model import ArcleInference
# Initialize unified omni engine (locates safetensors at repo root)
arcle = ArcleInference()
# 1. Text Dialogue & Reasoning
response = arcle.text("Explain the significance of on-device neural networks.")
print(response)
# 2. Vision Comprehension & VQA
arcle.vision("photo.jpg", "What objects are visible on the desk?")
# 3. Document OCR & Information Extraction
extracted_text = arcle.ocr("invoice.png")
# 4. Speech-to-Text (ASR)
transcription = arcle.audio("lecture.wav")
# 5. Text-to-Speech (TTS)
arcle.tts("Hello, welcome to Arcle Intelligence.", "output.wav")
# 6. Video Understanding
arcle.video("sample.mp4", "Summarize what happens in this clip.")
# 7. Text-to-Image Generation (Abstract Art)
arcle.image_gen("A celestial aurora over a crystalline ocean", "art.png")
Direct Low-Level Weights Loading
from model import load_flat
# Load all shards strictly with zero missing and zero unexpected weights
model = load_flat(".")
5. Technical Characteristics & Notes
Transparent Note on Image Generation
The image pathway conditions a diffusion transformer on the core language hidden states via a dedicated 65.7M parameter projection adapter. While the adapter reaches an embedding fit of $R^2 = 0.941$ that precisely captures prompt color, tone, contrast, and compositional density, it currently synthesizes sharp 1024×1024 abstract visual compositions rather than literal photorealistic subjects. For instance, prompting for a specific physical vehicle produces an abstract color composition reflecting the aesthetic mood rather than the physical object.
Hardware Requirements
- GPU Deployment: A GPU with 24 GB VRAM (e.g., RTX 3090, RTX 4090, A10, A5000) runs Arcle V1 comfortably in native
bfloat16(peak active memory 17.31 GB). - CPU Deployment: Supported in
float32mode; requires ~24 GB of system RAM. - Offline Guarantee: The TTS voice engine bundles
voices/am_adam.ptlocally. Verified withHF_HUB_OFFLINE=1.
6. Model Repository File Map
config.json Unified configuration manifest
model.safetensors.index.json Sharded weight routing table
ArcleIntelligence-core.safetensors Reasoning language core + LoRA adapter
ArcleIntelligence-connectors.safetensors All trained cross-modality connectors
ArcleIntelligence-vision.safetensors Vision comprehension encoder
ArcleIntelligence-ocr.safetensors Document structure encoder
ArcleIntelligence-audio.safetensors Speech recognition encoder
ArcleIntelligence-tts.safetensors Speech synthesis engine
ArcleIntelligence-generation.safetensors Image generation foundation
tokenizer.json, tokenizer_config.json Tokenizer specifications
chat_template.jinja Standardized chat format template
voices/am_adam.pt Offline voice checkpoint
Model Inferencing File/
model.py Full omni inference architecture
requirements.txt Dependency specification
README.md Runtime documentation
7. Citation & Attribution
If you use Arcle V1 in your research, edge applications, or development, please cite:
@misc{arcle2026v1,
author = {Abhinav Anand},
title = {Arcle V1: A Unified 5.84B Omni-Multimodal Foundation Model for Autonomous On-Device Intelligence},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Lucifer2006/Arcle-V1}}
}