Telescopium-Acyclic-Qwen3-0.6B

Telescopium-Acyclic-Qwen3-0.6B is a high-efficiency, multi-domain model fine-tuned on Qwen-0.6B using the rStar-Coder dataset enhanced with code expert clusters and an extended open code reasoning dataset, plus deepseek-r1 math reasoning traces. It leverages Directed Acyclic Graph (DAG) multistep reasoning for precise symbolic problem solving in mathematics, code, and science—making it ideal for developers, educators, and researchers working with structured reasoning pipelines under constrained compute.

GGUF: https://huggingface.co/prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B-GGUF

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Key Features

1. DAG-Based Multistep Reasoning for Math Implements Directed Acyclic Graph (DAG) reasoning methodology to break down complex mathematical problems into dependency-ordered steps, inspired by deepseek-r1 reasoning traces.

2. Unified Reasoning Across Code, Math & Science Fine-tuned on expert clusters spanning programming, mathematics, and scientific logic, alongside an open code reasoning dataset, enabling cross-domain symbolic precision.

3. Advanced Code Reasoning & Generation Supports multi-language coding with explanations, optimization hints, and error detection—ideal for full-stack prototyping, algorithm synthesis, and debugging workflows.

4. Scientific Problem Solving Performs analytical reasoning in physics, biology, and chemistry—explaining concepts, solving equations, and handling symbolic derivations step-by-step.

5. Hybrid Symbolic-AI Thinking Combines DAG logic decomposition, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM tasks and complex prompt decomposition.

6. Structured Output Mastery Seamlessly generates output in LaTeX, Markdown, JSON, CSV, and YAML, suited for research reports, technical documentation, and data formats.

7. Optimized Lightweight Footprint for Versatile Deployment Strikes a balance between performance and efficiency, making it deployable on mid-range GPUs, offline clusters, and advanced edge AI systems.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B"

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

prompt = "Solve the equation: 3x^2 + 5x - 2 = 0 using DAG-based step decomposition."

messages = [
    {"role": "system", "content": "You are a STEM reasoning tutor using DAG multistep methodology for problem solving."},
    {"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

• Mathematical tutoring with DAG-based decomposition
• Scientific and computational logic education
• Advanced coding assistant for algorithm design, code reviews, and documentation
• Structured technical data generation across formats and fields
• STEM-focused chatbot or API for research and education tools
• Mid-resource deployment requiring high symbolic fidelity

Limitations

• Not tuned for general-purpose or long-form creative writing
• Context limitations may hinder multi-document or full codebase analysis
• Specialized in technical and symbolic tasks—general chat may underperform
• Prioritizes structured reasoning over emotional or casual tone generation