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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ base_model:
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+ - Qwen/Qwen3-0.6B
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ tags:
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+ - text-generation-inference
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+ - DAG
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+ - gspo
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+ - trl
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+ - math
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+ - code
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+ ---
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+
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+ # **Telescopium-Acyclic-Qwen3-0.6B**
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+
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+ > **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.
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+
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+ > \[!note]
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+ > GGUF: [https://huggingface.co/prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B-GGUF](https://huggingface.co/prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B-GGUF)
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+
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+ ---
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+
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+ ## **Key Features**
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+
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+ 1. **DAG-Based Multistep Reasoning for Math**
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+ Implements **Directed Acyclic Graph (DAG) reasoning methodology** to break down complex mathematical problems into dependency-ordered steps, inspired by **deepseek-r1 reasoning traces**.
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+
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+ 2. **Unified Reasoning Across Code, Math & Science**
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+ Fine-tuned on **expert clusters** spanning programming, mathematics, and scientific logic, alongside an **open code reasoning dataset**, enabling cross-domain symbolic precision.
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+
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+ 3. **Advanced Code Reasoning & Generation**
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+ Supports multi-language coding with explanations, optimization hints, and error detection—ideal for full-stack prototyping, algorithm synthesis, and debugging workflows.
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+
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+ 4. **Scientific Problem Solving**
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+ Performs analytical reasoning in physics, biology, and chemistry—explaining concepts, solving equations, and handling symbolic derivations step-by-step.
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+
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+ 5. **Hybrid Symbolic-AI Thinking**
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+ Combines **DAG logic decomposition**, chain-of-thought reasoning, and open-ended inference, delivering robust performance on STEM tasks and complex prompt decomposition.
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+
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+ 6. **Structured Output Mastery**
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+ Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for research reports, technical documentation, and data formats.
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+
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+ 7. **Optimized Lightweight Footprint for Versatile Deployment**
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+ 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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+
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+ ---
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+
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+ ## **Quickstart with Transformers**
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype="auto",
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+ device_map="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ prompt = "Solve the equation: 3x^2 + 5x - 2 = 0 using DAG-based step decomposition."
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+
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+ messages = [
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+ {"role": "system", "content": "You are a STEM reasoning tutor using DAG multistep methodology for problem solving."},
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+ {"role": "user", "content": prompt}
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+ ]
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+
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+ text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+
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+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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+
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+ generated_ids = model.generate(
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+ **model_inputs,
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+ max_new_tokens=512
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+ )
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+ generated_ids = [
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+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+ ]
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+
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+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ print(response)
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+ ```
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+
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+ ---
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+
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+ ## **Intended Use**
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+
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+ * Mathematical tutoring with **DAG-based decomposition**
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+ * Scientific and computational logic education
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+ * Advanced coding assistant for algorithm design, code reviews, and documentation
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+ * Structured technical data generation across formats and fields
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+ * STEM-focused chatbot or API for research and education tools
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+ * Mid-resource deployment requiring high symbolic fidelity
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+
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+ ## **Limitations**
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+
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+ * Not tuned for general-purpose or long-form creative writing
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+ * Context limitations may hinder multi-document or full codebase analysis
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+ * Specialized in technical and symbolic tasks—general chat may underperform
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+ * Prioritizes structured reasoning over emotional or casual tone generation