---
license: apache-2.0
datasets:
- open-r1/Mixture-of-Thoughts
- nvidia/OpenCodeReasoning
language:
- en
base_model:
- prithivMLmods/Qwen3-1.7B-ft-bf16
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
- science
- moe
- code
---
 
![77.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/gwzyOkgaFO6AbSqT1_vMf.png)

# **Capricornus-MoT-1.7B-Supreme1**

> **Capricornus-MoT-1.7B-Supreme1** is a **high-precision, multi-domain expert model** fine-tuned from **Qwen3-1.7B**, built for **code generation**, **mathematical reasoning**, **scientific analysis**, and **open technical inference**. Trained on the **Mixture of Thoughts (MoT)** dataset with combined expert clusters in **code, math, and science**, and enhanced with an **Open Code Reasoning** dataset, it delivers powerful symbolic and structured outputs in a wide range of STEM and reasoning domains.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Capricornus-MoT-1.7B-Supreme1-GGUF](https://huggingface.co/prithivMLmods/Capricornus-MoT-1.7B-Supreme1-GGUF)

---

## **Key Features**

1. **Multi-Expert MoT Fine-Tuning**
   Fine-tuned on the **Mixture of Thoughts** dataset combining **code**, **math**, and **science** expert clusters, with added **Open Code Reasoning** for step-wise technical problem-solving and advanced symbolic thinking.

2. **Unified STEM Intelligence**
   Excels in algebra, calculus, scientific reasoning, and code logic—ideal for complex multi-step tasks, simulations, and educational applications.

3. **Advanced Code & Math Generation**
   Produces robust, readable code (Python, JavaScript, C++) with inline reasoning and debugging. Simultaneously capable of solving symbolic math and scientific problems with clarity.

4. **Structured Output Proficiency**
   Generates content in **Markdown**, **LaTeX**, **JSON**, and **YAML**—tailored for auto-documentation, data structuring, academic formats, and more.

5. **Multilingual & Multimodal Support**
   Handles technical prompts across **20+ languages** and adapts well to mixed-language code and STEM contexts for a global audience.

6. **Efficient 1.7B Inference Engine**
   Optimized for performance-to-power ratio—runs smoothly on consumer GPUs (e.g., 8–16GB VRAM), with elite-level results in symbolic tasks.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Capricornus-MoT-1.7B-Supreme1"

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

prompt = "Explain the code and solve the equation: Write a Python function to solve 2x + 3 = 11, and explain each step."

messages = [
    {"role": "system", "content": "You are an expert in math, code, and science reasoning."},
    {"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**

* Symbolic problem-solving in mathematics and science
* Intelligent code generation, analysis, and debugging
* Academic research assistants and structured STEM tutors
* Multilingual, structured output generation for documentation
* Ideal for developers, educators, and edge deployment in technical domains

---

## **Limitations**

* May not match performance of larger models on long-form generative or creative tasks
* Context window constraints affect large dataset or document processing
* Focused on STEM reasoning—free-form dialogue and general conversation are secondary
* Complex chaining tasks might require manual prompt engineering

---

## **References**

1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
3. [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts)
4. [Open Code Reasoning Dataset](https://huggingface.co/datasets/nvidia/OpenCodeReasoning)