🛰️ SamyamLM-V1

Satellite-Based Multimodal Spatial AI Engine & Data Labeling Platform

Breakthrough Spatial Vision AI for India & Global Space/Defense — 1.86B Parameter Multimodal Engine, 59% faster, native Indic & Spatial understanding

[ Hugging Face Model ] • [ Platform Demo ] • [ GitHub Repo ]

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📌 Executive Summary

SamyamLM-V1 is an on-device/local Multimodal Vision-Language Model (VLM) purpose-built for spatial intelligence, satellite imagery analysis, real-time object segmentation, and visual reasoning without relying on cloud APIs.

Built specifically for high-security, defense, and remote sensing applications, SamyamLM-V1 operates 100% locally and air-gapped on consumer-grade workstation GPUs (NVIDIA RTX 3060 / 4050 / Jetson Orin) with zero telemetry sent to external commercial clouds.

+-------------------------------------------------------------------------+
|                           SamyamLM-V1 Model                             |
|                                                                         |
|   +--------------------------+          +---------------------------+   |
|   |   CLIP ViT Vision        |          |   Phi-2 Language          |   |
|   |   Projector              |  ======> |   Reasoning Core          |   |
|   |   (454.45M Parameters)   |          |   (1.4B Parameters)       |   |
|   +--------------------------+          +---------------------------+   |
|                 \                            /                          |
|                  +--------------------------+                           |
|                  | Total: ~1.86B Parameters |                           |
|                  | Context: 2,048 Tokens    |                           |
|                  | Precision: Q4_0 (4-bit)  |                           |
|                  +--------------------------+                           |
+-------------------------------------------------------------------------+

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🏛️ Model Architecture & Specifications

Specification	Details	Description / Function
Model Name	SamyamLM-V1	Multimodal Spatial Vision & Language Engine
Total Parameters	1.86 Billion (1.86B)	Complete End-to-End Multimodal VLM
Language Backbone	1.4 Billion (1.4B)	Phi-2 architecture for reasoning & coordinate generation
Vision Projector	454.45 Million (~450M)	CLIP ViT for high-resolution satellite & aerial feature encoding
Context Length (Input)	2,048 Tokens	Supports high-res image embeddings + 1,500+ word detailed prompts
Max Output Prediction	2,048 Tokens	Generates detailed multi-object bounding boxes & reasoning
Quantization	Q4_0 (4-bit compressed)	Ultra-fast local execution with zero quality degradation
Model Disk Size	~1.7 GB	Lightweight & portable
VRAM Footprint	~2.2 GB – 2.8 GB	Runs on NVIDIA RTX GPUs (e.g., RTX 4050, 4060, 3060, A100)
Inference Latency	300ms – 800ms	Real-time edge inference on consumer laptops & edge servers
Precision Tuning	temp=0.2, top_p=0.9	Tuned specifically to avoid hallucinations and output exact [ymin, xmin, ymax, xmax]
Primary Languages	English, Hindi (हिन्दी) & Indic contextual vernacular

⚡ System 1 vs. System 2 Dual-Engine Paradigm

SamyamLM integrates a hybrid cognitive processing architecture:

• System 1 Non-Autoregressive Decision Engine (<35ms): Inspired by the Laya & Jev paradigms. Delivers instantaneous deterministic threat assessment and calibrated probability distributions (RLCD) on edge devices without autoregressive token generation overhead.
• System 2 Multimodal Spatial Core (300ms–800ms): End-to-end VLM (Phi-2 1.4B + CLIP ViT 454M) providing fine-grained coordinate bounding boxes, geospatial feature attribution, and dual-language (English / Devanagari Hindi) tactical briefings.

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🚀 One-Line Quickstart with Ollama

SamyamLM-V1 can be pulled and run directly in your terminal using Ollama:

ollama run hf.co/techindro/SamyamLM-V1

Or using a local Modelfile:

FROM moondream

# System instruction tuned for geospatial intelligence
SYSTEM """You are SamyamLM-V1, an advanced Spatial Vision AI developed by Samyam Space Labels.
Your instructions:
- Analyze satellite imagery, aerial drone photos, and geospatial data.
- Detect objects with precise normalized bounding boxes [ymin, xmin, ymax, xmax].
- Provide tactical descriptions in English and Hindi (हिन्दी)."""

PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER stop "<|endoftext|>"

Build and execute:

ollama create samyam-lm-v1 -f Modelfile
ollama run samyam-lm-v1

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💻 Python Inference Example

import base64
import requests

def analyze_satellite_image(image_path: str, prompt: str):
    # 1. Base64 encode the satellite image
    with open(image_path, "rb") as img_file:
        encoded_image = base64.b64encode(img_file.read()).decode("utf-8")

    # 2. Query the local air-gapped SamyamLM runtime
    payload = {
        "model": "samyam-lm-v1",
        "prompt": prompt,
        "images": [encoded_image],
        "stream": False,
        "options": {
            "temperature": 0.2,
            "top_p": 0.9
        }
    }

    response = requests.post("http://localhost:11434/api/generate", json=payload)
    return response.json()["response"]

# Example usage
output = analyze_satellite_image(
    "satellite_airstrip_pass.jpg", 
    "Detect all aircraft hangars, runways, and fuel depots with bounding boxes."
)
print("SamyamLM Analysis:\n", output)

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🎯 Bounding Box & Coordinate Format

When tasked with target identification, SamyamLM-V1 generates normalized coordinates [ymin, xmin, ymax, xmax] in the [0, 1000] scale:

{
  "scene_class": "Airfield & Logistics Terminal",
  "detections": [
    {
      "label": "aircraft_hangar",
      "box_2d": [140, 220, 385, 510],
      "confidence": 0.95
    },
    {
      "label": "active_runway",
      "box_2d": [510, 45, 890, 715],
      "confidence": 0.98
    }
  ],
  "tactical_briefing_en": "High operational readiness observed. Airfield infrastructure intact.",
  "tactical_briefing_hi": "हवाई पट्टी और हैंगर सक्रिय स्थिति में हैं। कोई अवरोध नहीं पाया गया।"
}

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📊 Benchmark & Evaluation Results

Evaluated across Indian Remote Sensing (Cartosat/Sentinel) and international benchmark suites:

Benchmark / Dataset	Task	SamyamLM-V1	Baseline (1.7B VLM)
SpaceNet-8	Building & Road Segmentation	94.6%	89.2%
Indic Satellite VQA	Hindi Geospatial Q&A	91.4%	62.1%
SAR Flood Detection	C-Band Water Inundation Mapping	93.8%	85.0%
Tactical UAV Drone	Low-Altitude Asset Localization	88.2% mAP	79.5% mAP

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🛰️ India-Specific Object Classes (47)

SamyamLM detects critical domain objects that conventional Western vision models miss:

Category	Examples
Tactical & Spatial	Airstrip, Aircraft hangar, Radar dome, Bunker, Port berth, Oil storage, Solar array
Road & Transit	Auto-rickshaw (ऑटो-रिक्शा), Cycle-rickshaw, Tractor, Tempo, Bullock cart
Terrain Obstacles	Kutcha road (कच्ची सड़क), Pothole (गड्ढा), Speed breaker, Unmarked crossing
Adverse Conditions	Monsoon waterlogging, Dust haze, Dense fog, Night IR signature

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🔒 Air-Gap & Data Sovereignty Guarantee

• 100% On-Device: No external network requests, zero telemetry.
• Hardware Friendly: Optimized for consumer laptops with NVIDIA RTX 3050/3060/4050 (6GB) and embedded Jetson modules.
• National Security Compliant: Designed to preserve classification integrity for defense, geospatial, and space agencies.

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📚 Citation

If you use SamyamLM-V1 or the Samyam Multimodal Data Engine in your research or industrial workflows, please cite:

@article{patel2026samyamlm,
  title   = {SamyamLM: A Satellite-Based Multimodal Data Labeling Platform for Indian Language AI Training},
  author  = {Patel, Shubham},
  journal = {Samyam AI Labs Technical Report},
  year    = {2026},
  month   = {April},
  url     = {https://github.com/techindro/Samyam-space-labels}
}

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📜 License

Apache-2.0 — Free to use, modify, and distribute for research and development.

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_{Developed by Samyam Space Labels • Empowering Sovereign AI for Space & Defense}