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Model: prithivMLmods/Canum-Qwen3_R1-4B-iCoT Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- TAUR-dev/STEPS__r1_4d_eval__mini_all
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- TAUR-dev/STEPS__r1_8d_eval__v3_mini_all
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- TAUR-dev/STEPS__r1_8d_eval__v4
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- TAUR-dev/STEPS__r1_8d_eval__v3_4o
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language:
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- en
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library_name: transformers
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base_model:
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- prithivMLmods/Qwen3-4B-ft-bf16
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pipeline_tag: text-generation
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tags:
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- text-generation-inference
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- trl
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- moe
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- code
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- math
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---
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# Canum-Qwen3\_R1-4B-iCoT
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> **Canum-Qwen3\_R1-4B-iCoT** is a precision-tuned variant of the Qwen3-4B architecture, explicitly aligned with **internal chain-of-thought (iCoT)** methodologies. Trained on the **TAUR-dev/STEPS\_\_r1\_4d\_eval\_\_mini\_all** dataset, this model excels in **long-form mathematical reasoning**, **progressive symbolic logic**, and **multi-stage problem decomposition**, all within a compact 4B parameter footprint.
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> [!note]
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GGUF : https://huggingface.co/prithivMLmods/Canum-Qwen3_R1-4B-iCoT-Q4_K_M-GGUF
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## Key Features
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1. **Internal Chain-of-Thought Reasoning (iCoT)**
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Enables deeper logical progression through internally coherent reasoning steps, ideal for complex mathematical derivations and multivariable algebraic thinking.
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2. **Dataset: TAUR-dev/STEPS\_\_r1\_4d\_eval\_\_mini\_all**
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Fine-tuned using structured evaluation sequences to build resilience in multi-step problem solving and improve interpretability in math-focused tasks.
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3. **Long Reasoning Paths in STEM Domains**
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Suited for long-chain logical flows in geometry, number theory, calculus, and symbolic manipulation, including proofs and multi-stage equation solving.
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4. **Lightweight Yet Capable (4B)**
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Maintains strong reasoning and instruction-following abilities with lower computational cost compared to larger models, suitable for single-GPU deployments.
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5. **Instruction-Following and Step-by-Step Alignment**
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Follows complex instructions with multi-turn dependencies and provides granular output that aligns with internal steps used in the reasoning process.
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6. **Technical Format Adaptability**
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Outputs answers in clean Markdown, LaTeX, JSON, or table formats for academic, development, and notebook-based use cases.
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## Quickstart with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Canum-Qwen3_R1-4B-iCoT"
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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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prompt = "Use internal CoT to solve: A rectangle has a length that is 3 times its width. If the perimeter is 48 units, what are the dimensions?"
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messages = [
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{"role": "system", "content": "You are a reasoning assistant trained to use internal chain-of-thought (iCoT) for multi-step mathematical problems."},
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{"role": "user", "content": prompt}
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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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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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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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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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## Intended Use
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* Internal chain-of-thought (iCoT) problem solving
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* Long-form symbolic math and algebraic derivations
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* Curriculum-based step-by-step math tutoring
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* Structured multi-turn reasoning in STEM domains
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* Output generation in technical formats (LaTeX, Markdown)
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## Limitations
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* May require well-structured prompts for optimal reasoning output
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* Smaller context length may limit extremely long multi-part problems
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* Focused on precision reasoning, not creative or subjective writing
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* Best used with prompt patterns that guide internal logical steps
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## References
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1. **TAUR-dev/STEPS\_\_r1\_4d\_eval\_\_mini\_all** – Dataset for structured math reasoning
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2. **Internal CoT (iCoT)** – Progressive logical strategy for complex problems
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3. [AIMO-2 Math Benchmark – OpenMathReasoning](https://arxiv.org/pdf/2504.16891)
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4. [YaRN: Efficient Context Extension of LLMs](https://arxiv.org/pdf/2309.00071)
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