Instructions to use Anoopsingh53/ISRO-SpaceAI-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anoopsingh53/ISRO-SpaceAI-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Anoopsingh53/ISRO-SpaceAI-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Anoopsingh53/ISRO-SpaceAI-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Anoopsingh53/ISRO-SpaceAI-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anoopsingh53/ISRO-SpaceAI-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anoopsingh53/ISRO-SpaceAI-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anoopsingh53/ISRO-SpaceAI-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anoopsingh53/ISRO-SpaceAI-7B-Instruct

SGLang

How to use Anoopsingh53/ISRO-SpaceAI-7B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Anoopsingh53/ISRO-SpaceAI-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anoopsingh53/ISRO-SpaceAI-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Anoopsingh53/ISRO-SpaceAI-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anoopsingh53/ISRO-SpaceAI-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anoopsingh53/ISRO-SpaceAI-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Anoopsingh53/ISRO-SpaceAI-7B-Instruct
```

ISRO-SpaceAI-7B-Instruct / README.md

Anoopsingh53

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	---
	language:
	- en
	- hi
	license: apache-2.0
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- space
	- isro
	- nasa
	- aditya-l1
	- chandrayaan-3
	- oceansat-3
	- calcofi
	- oceanography
	- sentinel-1
	- sar
	- radar
	- flood
	- astrophysics
	- astronomy
	- cosmology
	- remote-sensing
	- kepler
	- exoplanet
	- heliophysics
	- qlora
	- fp16
	- text-generation
	datasets:
	- UniverseTBD/arxiv-qa-astro-ph
	- Anoopsingh53/isro-space-ocean-dataset
	pipeline_tag: text-generation
	library_name: transformers
	model-index:
	- name: ISRO-SpaceAI-7B-Instruct
	results:
	- task:
	type: text-generation
	name: Empirical Forward-Pass Domain Benchmark
	dataset:
	name: ISRO Space & Ocean Dataset Test Split
	type: Anoopsingh53/isro-space-ocean-dataset
	metrics:
	- name: Oceanography Token Accuracy
	type: accuracy
	value: 59.42%
	- name: Oceanography Validation Perplexity
	type: perplexity
	value: 8.58
	- name: Heliophysics Token Accuracy
	type: accuracy
	value: 53.85%
	- name: Heliophysics Validation Perplexity
	type: perplexity
	value: 10.47
	- name: Astrophysics Token Accuracy
	type: accuracy
	value: 53.17%
	- name: Astrophysics Validation Perplexity
	type: perplexity
	value: 10.76
	---

	<div align="center">

	# 🛰️ ISRO-SpaceAI-7B-Instruct
	### India's First Empirical Multi-Domain Foundation Model for Heliophysics, Oceanography & Planetary Observation

	[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Base Model](https://img.shields.io/badge/Base_Architecture-Qwen_2.5_7B_Instruct-792ee5.svg)](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
	[![Precision](https://img.shields.io/badge/Precision-Full_FP16_SafeMerge-00c853.svg)]()
	[![Context](https://img.shields.io/badge/Context_Length-32%2C768_Tokens-0288d1.svg)]()
	[![Dataset](https://img.shields.io/badge/Dataset_Hub-isro--space--ocean-cyan.svg)](https://huggingface.co/datasets/Anoopsingh53/isro-space-ocean-dataset)
	[![Event](https://img.shields.io/badge/ISRO_Submission-National_Space_Day_2026-gold.svg)]()

	[Model Card](#executive-summary) • [Empirical Benchmarks](#official-empirical-domain-benchmarks) • [Architecture Specs](#model-architecture-specifications) • [Deployment](#quickstart--deployment) • [Citation](#citation)

	</div>

	---

	## 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.

	---

	## 📊 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.

	---

	## 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 \|

	---

	## 🌐 4 Integrated Multi-Domain Research Pillars

	```mermaid
	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"]
	```

	---

	## Quickstart & Deployment

	### 1. PyTorch & Hugging Face Transformers

	```python
	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))
	```

	---

	## 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.

	---

	## 🏛️ 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](https://huggingface.co/Anoopsingh53))
	- Official Dataset Hub: [`Anoopsingh53/isro-space-ocean-dataset`](https://huggingface.co/datasets/Anoopsingh53/isro-space-ocean-dataset)

	---

	## Citation

	```bibtex
	@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}
	}
	```