Instructions to use prithivMLmods/Eta-Aurigae-0.6B-Echelon1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Eta-Aurigae-0.6B-Echelon1 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Eta-Aurigae-0.6B-Echelon1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Eta-Aurigae-0.6B-Echelon1")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Eta-Aurigae-0.6B-Echelon1")
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 prithivMLmods/Eta-Aurigae-0.6B-Echelon1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Eta-Aurigae-0.6B-Echelon1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Eta-Aurigae-0.6B-Echelon1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Eta-Aurigae-0.6B-Echelon1

SGLang

How to use prithivMLmods/Eta-Aurigae-0.6B-Echelon1 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 "prithivMLmods/Eta-Aurigae-0.6B-Echelon1" \
    --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": "prithivMLmods/Eta-Aurigae-0.6B-Echelon1",
		"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 "prithivMLmods/Eta-Aurigae-0.6B-Echelon1" \
        --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": "prithivMLmods/Eta-Aurigae-0.6B-Echelon1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Eta-Aurigae-0.6B-Echelon1 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Eta-Aurigae-0.6B-Echelon1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Eta-Aurigae-0.6B-Echelon1

Eta-Aurigae-0.6B-Echelon1 is a compact, efficient model specialized in science, factual accuracy, and structured reasoning. Fine-tuned on Qwen3-0.6B using the MoT (Mixture of Thoughts) dataset—focused on scientific understanding and expert factual domains—it delivers high-precision outputs for STEM education, tutoring, and analytical thinking in resource-constrained environments.

GGUF: https://huggingface.co/prithivMLmods/Eta-Aurigae-0.6B-Echelon1-GGUF

Key Features

MoT Fine-Tuning for Science & Facts Trained on a Mixture of Thoughts dataset emphasizing scientific accuracy, explanatory depth, and structured reasoning across biology, physics, chemistry, and factual domains.
Scientific Precision in a Small Footprint Delivers clear, step-by-step reasoning in scientific problems—ideal for students, educators, and lightweight educational tools.
Factually Consistent Output Generation Optimized for high factual alignment and structured explanations—reliable for knowledge recall, concept breakdowns, and factual analysis.
Supports Markdown, LaTeX, and JSON Outputs clean, structured formats like Markdown, LaTeX, and JSON, useful for technical documentation and educational content.
Multilingual Science-Aware Responses Handles factual content in 20+ languages, especially in academic and technical contexts.
Lightweight and Inference-Ready Efficient on CPUs, low-VRAM GPUs, and offline edge deployments without sacrificing factual clarity.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Eta-Aurigae-0.6B-Echelon1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "What causes the northern lights (Aurora Borealis)? Explain in simple terms."

messages = [
    {"role": "system", "content": "You are a science tutor that explains complex concepts clearly."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)