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

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Key Features

1. 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.

2. Scientific Precision in a Small Footprint Delivers clear, step-by-step reasoning in scientific problems—ideal for students, educators, and lightweight educational tools.

3. Factually Consistent Output Generation Optimized for high factual alignment and structured explanations—reliable for knowledge recall, concept breakdowns, and factual analysis.

4. Supports Markdown, LaTeX, and JSON Outputs clean, structured formats like Markdown, LaTeX, and JSON, useful for technical documentation and educational content.

5. Multilingual Science-Aware Responses Handles factual content in 20+ languages, especially in academic and technical contexts.

6. Lightweight and Inference-Ready Efficient on CPUs, low-VRAM GPUs, and offline edge deployments without sacrificing factual clarity.

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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)

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Intended Use

• Science education and fact-based tutoring
• Concept explanations in physics, biology, and chemistry
• Structured technical content generation (e.g., LaTeX, Markdown)
• Deployment in low-resource, educational, or mobile scenarios
• Lightweight inference with high factual fidelity

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Limitations

• Not optimized for general conversation or creative writing
• Short context limits multi-document scientific reasoning
• Performance dips in abstract reasoning outside scientific scope
• Not tuned for code or free-form generation

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References

1. Qwen2.5 Technical Report (2024)
2. Mixture of Thoughts Dataset
3. YaRN: Efficient Context Extension for LLMs