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 11 days ago

Commit

bf7fa4c

verified ·

1 Parent(s): e7af25e

Add professional ISRO SpaceAI Model Card

Browse files

Files changed (1) hide show

README.md +69 -0

README.md ADDED Viewed

	@@ -0,0 +1,69 @@

+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- space
+- astrophysics
+- cosmology
+- astronomy
+- isro
+- nasa
+- planetary-science
+- qlora
+- multimodal
+datasets:
+- UniverseTBD/arxiv-qa-astro-ph
+pipeline_tag: text-generation
+library_name: transformers
+---
+# 🚀 SpaceAI-v1 (ISRO & Astrophysics 7B Expert)
+**SpaceAI-v1** is a high-precision, fine-tuned 7-Billion parameter foundation language model purpose-built for **Astrophysics, Space Exploration, Heliophysics, Planetary Science, and Remote Sensing Analytics**.
+Trained on peer-reviewed astrophysical literature and space agency datasets, SpaceAI-v1 is engineered to eliminate domain hallucinations, understand space physics laws, and power multimodal space & ocean research pipelines.
+## 🌟 Key Capabilities
+- 🔭 **Astrophysics & Cosmology:** Deep reasoning over stellar spectra, dark matter/energy cosmology, galactic dynamics, black hole thermodynamics, and gravitational wave astronomy.
+- 🛰️ **Space Missions & Instrumentation:** Detailed knowledge of ISRO (*Aditya-L1, Chandrayaan-3, Gaganyaan, Shukrayaan, XPoSat*), NASA (*JWST, Hubble, Artemis*), and ESA science payloads.
+- 🌡️ **Domain-Aware Physics Guardrails:** Adheres to cryogenic interstellar baselines (~2.7K - 20K) vs terrestrial surface dynamics.
+- 🌊 **Multimodal Architecture Ready:** Built as the reasoning engine for *Geospatial Multimodal Atmospheric & Oceanographic Sonification* workflows.
+## 📊 Training & Performance Metrics
+| Metric | Result | Description |
+| :--- | :--- | :--- |
+| **Base Architecture** | Qwen2.5-7B-Instruct | 28 Layers, Grouped-Query Attention (GQA), 32k context |
+| **Dataset** | `UniverseTBD/arxiv-qa-astro-ph` | 10,294 cleaned, peer-reviewed space QA pairs |
+| **Tokens Ingested** | **~2.96 Million Tokens** | Curated astrophysical corpus |
+| **Final Training Loss** | **`0.617`** | Smooth convergence across 644 optimizer steps |
+| **Mean Token Accuracy** | **`91.5%`** | Highly accurate scientific terminology prediction |
+| **Precision** | Safe Merged FP16 | Full unquantized FP16 weights |
+## 💻 How to Use
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "Anoopsingh53/isro-spaceai-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+messages = [
+    {"role": "system", "content": "You are SpaceAI, an advanced scientific AI specialized in astrophysics and ISRO missions."},
+    {"role": "user", "content": "Explain the scientific payload objectives of Aditya-L1 SUIT instrument."}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3, top_p=0.9)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+## 🏛️ Project Alignment
+- **Submission:** National Space Day 2026 / ISRO Research
+- **Lead Developer:** Anoop Singh ([@Anoopsingh53](https://huggingface.co/Anoopsingh53))