---
license: apache-2.0
datasets:
- TAUR-dev/STEPS__r1_4d_eval__mini_all
- TAUR-dev/STEPS__r1_8d_eval__v3_mini_all
- TAUR-dev/STEPS__r1_8d_eval__v4
- TAUR-dev/STEPS__r1_8d_eval__v3_4o
language:
- en
library_name: transformers
base_model:
- prithivMLmods/Qwen3-4B-ft-bf16
pipeline_tag: text-generation
tags:
- text-generation-inference
- trl
- moe
- code
- math
---

![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WcV2TMjLc0yW49uCYdE2k.png)

# Canum-Qwen3\_R1-4B-iCoT

> **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.

> [!note]
GGUF : https://huggingface.co/prithivMLmods/Canum-Qwen3_R1-4B-iCoT-Q4_K_M-GGUF


## Key Features

1. **Internal Chain-of-Thought Reasoning (iCoT)**
   Enables deeper logical progression through internally coherent reasoning steps, ideal for complex mathematical derivations and multivariable algebraic thinking.

2. **Dataset: TAUR-dev/STEPS\_\_r1\_4d\_eval\_\_mini\_all**
   Fine-tuned using structured evaluation sequences to build resilience in multi-step problem solving and improve interpretability in math-focused tasks.

3. **Long Reasoning Paths in STEM Domains**
   Suited for long-chain logical flows in geometry, number theory, calculus, and symbolic manipulation, including proofs and multi-stage equation solving.

4. **Lightweight Yet Capable (4B)**
   Maintains strong reasoning and instruction-following abilities with lower computational cost compared to larger models, suitable for single-GPU deployments.

5. **Instruction-Following and Step-by-Step Alignment**
   Follows complex instructions with multi-turn dependencies and provides granular output that aligns with internal steps used in the reasoning process.

6. **Technical Format Adaptability**
   Outputs answers in clean Markdown, LaTeX, JSON, or table formats for academic, development, and notebook-based use cases.

## Quickstart with Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Canum-Qwen3_R1-4B-iCoT"

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

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?"

messages = [
    {"role": "system", "content": "You are a reasoning assistant trained to use internal chain-of-thought (iCoT) for multi-step mathematical problems."},
    {"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)
```

## Intended Use

* Internal chain-of-thought (iCoT) problem solving
* Long-form symbolic math and algebraic derivations
* Curriculum-based step-by-step math tutoring
* Structured multi-turn reasoning in STEM domains
* Output generation in technical formats (LaTeX, Markdown)

## Limitations

* May require well-structured prompts for optimal reasoning output
* Smaller context length may limit extremely long multi-part problems
* Focused on precision reasoning, not creative or subjective writing
* Best used with prompt patterns that guide internal logical steps

## References

1. **TAUR-dev/STEPS\_\_r1\_4d\_eval\_\_mini\_all** – Dataset for structured math reasoning
2. **Internal CoT (iCoT)** – Progressive logical strategy for complex problems
3. [AIMO-2 Math Benchmark – OpenMathReasoning](https://arxiv.org/pdf/2504.16891)
4. [YaRN: Efficient Context Extension of LLMs](https://arxiv.org/pdf/2309.00071)