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
```

Anoopsingh53 commited on 10 days ago

Commit

44709e0

verified ·

1 Parent(s): 95e033b

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +10 -10

README.md CHANGED Viewed

@@ -33,7 +33,7 @@ datasets:
 pipeline_tag: text-generation
 library_name: transformers
 model-index:
-- name: SpaceAI-v1.1-Enterprise
   results:
   - task:
       type: text-generation
@@ -58,7 +58,7 @@ model-index:
 <div align="center">
-# 🛰️ SpaceAI-v1.1: ISRO & NASA Multi-Domain 7B Foundation Model
 ### **An Empirical Foundation Intelligence for Heliophysics, Marine Hydrosphere & Planetary Observation**
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
@@ -76,7 +76,7 @@ model-index:
 ## Executive Abstract
-**SpaceAI-v1.1** is a 7.61-Billion parameter foundation language model purpose-built for scientific reasoning, telemetry analysis, and predictive physical modeling across **Solar Heliophysics (ISRO Aditya-L1), Marine Oceanography (CalCOFI / Oceansat-3), Microwave Earth Observation (Sentinel-1 SAR), and Exoplanetary Photometry (NASA Kepler)**.
 Fine-tuned on curated astrophysical literature and multi-spectral sensor manifolds via **4-bit NormalFloat (NF4) QLoRA with full IEEE FP16 weight safe-merging**, SpaceAI delivers high factual grounding, sub-second latency, and zero-hallucination domain inference.
@@ -106,7 +106,7 @@ Evaluated across standardized scientific NLP benchmarks, out-of-distribution dom
 ### 1. NLP & Scientific Reasoning Benchmarks (Unseen Test Split, $N=1{,}024$)
-| Evaluation Metric | Baseline (Qwen 2.5 7B Base) | SpaceAI-v1.1 (Fine-Tuned) | Delta Improvement |
 | :--- | :---: | :---: | :---: |
 | **AstroQA Domain F1-Score** | 72.35% | **89.12%** | **+16.77%** |
 | **AstroQA Exact Match (EM)** | 58.12% | **76.45%** | **+18.33%** |
@@ -145,7 +145,7 @@ Comparison across equivalent 7B–8B parameter open-weights models on domain sci
 | **Llama-3-8B-Instruct** | 8.0B | 73.80% | 69.20% | 0.742 | 8,192 |
 | **Mistral-7B-Instruct-v0.3** | 7.2B | 71.45% | 66.85% | 0.710 | 32,768 |
 | **Qwen-2.5-7B-Base** | 7.6B | 72.35% | 68.10% | 0.765 | 32,768 |
-| **SpaceAI-v1.1 (Ours)** | **7.6B** | **89.12%** | **78.34%** | **0.941** | **32,768** |
 ---
@@ -169,7 +169,7 @@ graph TD
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "Anoopsingh53/isro-spaceai-v1"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
@@ -181,7 +181,7 @@ model = AutoModelForCausalLM.from_pretrained(
 conversation = [
     {
         "role": "system",
-        "content": "You are SpaceAI-v1.1, an empirical scientific intelligence specialized in ISRO/NASA heliophysics, oceanography, and remote sensing."
     },
     {
         "role": "user",
@@ -208,7 +208,7 @@ print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_toke
 ```bash
 python -m vllm.entrypoints.openai.api_server \
-    --model Anoopsingh53/isro-spaceai-v1 \
     --tensor-parallel-size 1 \
     --dtype float16 \
     --max-model-len 8192 \
@@ -239,10 +239,10 @@ python -m vllm.entrypoints.openai.api_server \
 ```bibtex
 @misc{singh2026spaceai,
   author       = {Singh, Anoop},
-  title        = {SpaceAI-v1.1: An Empirical Multimodal Foundation Model for Heliophysics, Oceanography, and Planetary Observation},
   year         = {2026},
   publisher    = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Anoopsingh53/isro-spaceai-v1}},
   note         = {National Space Day 2026 ISRO/IN-SPACe Contribution}
 }
 ```

 pipeline_tag: text-generation
 library_name: transformers
 model-index:
+- name: ISRO-SpaceAI-7B-Instruct-Enterprise
   results:
   - task:
       type: text-generation
 <div align="center">
+# 🛰️ ISRO-SpaceAI-7B-Instruct: ISRO & NASA Multi-Domain 7B Foundation Model
 ### **An Empirical Foundation Intelligence for Heliophysics, Marine Hydrosphere & Planetary Observation**
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 ## Executive Abstract
+**ISRO-SpaceAI-7B-Instruct** is a 7.61-Billion parameter foundation language model purpose-built for scientific reasoning, telemetry analysis, and predictive physical modeling across **Solar Heliophysics (ISRO Aditya-L1), Marine Oceanography (CalCOFI / Oceansat-3), Microwave Earth Observation (Sentinel-1 SAR), and Exoplanetary Photometry (NASA Kepler)**.
 Fine-tuned on curated astrophysical literature and multi-spectral sensor manifolds via **4-bit NormalFloat (NF4) QLoRA with full IEEE FP16 weight safe-merging**, SpaceAI delivers high factual grounding, sub-second latency, and zero-hallucination domain inference.
 ### 1. NLP & Scientific Reasoning Benchmarks (Unseen Test Split, $N=1{,}024$)
+| Evaluation Metric | Baseline (Qwen 2.5 7B Base) | ISRO-SpaceAI-7B-Instruct (Fine-Tuned) | Delta Improvement |
 | :--- | :---: | :---: | :---: |
 | **AstroQA Domain F1-Score** | 72.35% | **89.12%** | **+16.77%** |
 | **AstroQA Exact Match (EM)** | 58.12% | **76.45%** | **+18.33%** |
 | **Llama-3-8B-Instruct** | 8.0B | 73.80% | 69.20% | 0.742 | 8,192 |
 | **Mistral-7B-Instruct-v0.3** | 7.2B | 71.45% | 66.85% | 0.710 | 32,768 |
 | **Qwen-2.5-7B-Base** | 7.6B | 72.35% | 68.10% | 0.765 | 32,768 |
+| **ISRO-SpaceAI-7B-Instruct (Ours)** | **7.6B** | **89.12%** | **78.34%** | **0.941** | **32,768** |
 ---
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "Anoopsingh53/ISRO-SpaceAI-7B-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
 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",
 ```bash
 python -m vllm.entrypoints.openai.api_server \
+    --model Anoopsingh53/ISRO-SpaceAI-7B-Instruct \
     --tensor-parallel-size 1 \
     --dtype float16 \
     --max-model-len 8192 \
 ```bibtex
 @misc{singh2026spaceai,
   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}
 }
 ```