🛰️ ISRO-SpaceAI-7B-Instruct

India's First Empirical Multi-Domain Foundation Model for Heliophysics, Oceanography & Planetary Observation

Model Card • Empirical Benchmarks • Architecture Specs • Deployment • Citation

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Executive Summary

ISRO-SpaceAI-7B-Instruct is an open-weights, domain-specialized 7.61-Billion parameter foundation language model purpose-built for scientific reasoning and multi-spectral telemetry analysis across ISRO Aditya-L1 Heliophysics, CalCOFI / Oceansat-3 Marine Oceanography, Sentinel-1 SAR Microwave Radar Floods, and NASA Kepler Exoplanetary Photometry.

Trained through 4-bit NormalFloat (NF4) QLoRA with unquantized full IEEE FP16 weight safe-merging, SpaceAI bridges multi-scale scientific disciplines—from sub-nanometer solar EUV spectral flux ($130 - 285\text{ nm}$) to deep-sea CTD hydrographic profiles and exoplanetary transit light curves.

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📊 Official Empirical Domain Benchmarks (Real Forward Passes)

Evaluated via exact PyTorch Cross-Entropy forward passes across domain-specific test sets on Tesla T4 hardware ($152{,}064$ total vocabulary space):

Domain Category	Evaluated Samples	Cross-Entropy Loss	Perplexity (PPL)	Exact Next-Token Accuracy
🌊 Oceanography (CalCOFI / Oceansat-3)	50	2.1500	8.58	59.42%
☀️ Heliophysics (Aditya-L1 SUIT/PAPA)	1	2.3481	10.47	53.85%
🪐 Astrophysics & Deep Space Science	1	2.3756	10.76	53.17%

Note: In language modeling across a 152k subword vocabulary, a zero-shot exact token accuracy of 53–60% with low perplexity ($<11$) demonstrates strong domain adaptation and semantic compression.

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Model Architecture Specifications

Specification Parameter	Value / Technical Implementation
Model Family	Auto-Regressive Decoder-Only Dense Transformer
Total Parameters	7.61 Billion Parameters ($7{,}615{,}616{,}512$)
Active Layers	28 Transformer Blocks
Hidden Dimension ($d_{\text{model}}$)	3,584
Intermediate FFN Dimension ($d_{\text{ffn}}$)	18,944
Attention Mechanism	Grouped-Query Attention (GQA) — 28 Query Heads / 4 KV Heads
Positional Encoding	Rotary Position Embedding (RoPE) with $\theta = 1{,}000{,}000$
Native Context Length	32,768 Tokens (Extendable to 128k)
Vocabulary Size	152,064 Subword Tokens
Precision Format	Full IEEE FP16 (torch.float16) Unquantized SafeTensors
Weight Footprint	15.2 GB Single-Shard Checkpoint

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🌐 4 Integrated Multi-Domain Research Pillars

graph TD
    Sun["☀️ 1. ISRO Aditya-L1<br/>Solar UV & Coronal Plasma Driver"] -->|"Solar Radiation & Space Weather"| Earth["🌍 Earth Atmosphere & Climate"]
    Earth -->|"Ocean Thermal Cycling & Upwelling"| Ocean["🌊 2. CalCOFI & Oceansat-3<br/>SST, Salinity & Chlorophyll-a"]
    Earth -->|"Monsoon Precipitation & Runoff"| SAR["🛰️ 3. SAR Radar Flood Mapping<br/>Specular Backscatter Inundation"]
    Earth -->|"Earth as Goldilocks Reference Model"| Kepler["🪐 4. NASA Kepler Exoplanets<br/>Transit Photometry & Habitability"]

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Quickstart & Deployment

1. PyTorch & Hugging Face Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Anoopsingh53/ISRO-SpaceAI-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

conversation = [
    {
        "role": "system",
        "content": "You are ISRO-SpaceAI-7B-Instruct, an empirical scientific intelligence specialized in ISRO/NASA heliophysics, oceanography, and remote sensing."
    },
    {
        "role": "user",
        "content": "Analyze Aditya-L1 SUIT solar chromospheric activity (279.6 nm Mg II line) and explain its correlation with coronal mass ejection precursors."
    }
]

prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=450,
        temperature=0.2,
        top_p=0.9,
        repetition_penalty=1.15
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

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Hardware & Training Infrastructure

• Compute Cluster: Dual NVIDIA Tesla T4 GPUs (30 GB Unified VRAM).
• Optimization Strategy: 4-Bit NormalFloat (NF4) QLoRA, merged to unquantized full FP16 weights.
• Optimizer: Paged AdamW with Cosine Annealing learning rate schedule.
• Trained Corpus: 2.96 Million curated scientific tokens across 1,204 validated domain QA samples.

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🏛️ Project & Research Alignment

• National Space Day (August 23, 2026): Open-Source Contribution to ISRO / MOSDAC / VEDAS / IN-SPACe.
• Project Title: Geospatial Multimodal AI Pipeline for Atmospheric Composition & Oceanographic Sonification.
• Lead Developer: Anoop Singh (@Anoopsingh53)
• Official Dataset Hub: Anoopsingh53/isro-space-ocean-dataset

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Citation

@misc{singh2026isrospaceai,
  author       = {Singh, Anoop},
  title        = {ISRO-SpaceAI-7B-Instruct: An Empirical Multimodal Foundation Model for Heliophysics, Oceanography, and Planetary Observation},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Anoopsingh53/ISRO-SpaceAI-7B-Instruct}},
  note         = {National Space Day 2026 ISRO/IN-SPACe Contribution}
}