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Model: prithivMLmods/Capricornus-MoT-1.7B-Supreme1 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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- open-r1/Mixture-of-Thoughts
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- nvidia/OpenCodeReasoning
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language:
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- en
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base_model:
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- prithivMLmods/Qwen3-1.7B-ft-bf16
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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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- math
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- science
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- moe
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- code
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---
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# **Capricornus-MoT-1.7B-Supreme1**
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> **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.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Capricornus-MoT-1.7B-Supreme1-GGUF](https://huggingface.co/prithivMLmods/Capricornus-MoT-1.7B-Supreme1-GGUF)
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---
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## **Key Features**
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1. **Multi-Expert MoT Fine-Tuning**
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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.
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2. **Unified STEM Intelligence**
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Excels in algebra, calculus, scientific reasoning, and code logic—ideal for complex multi-step tasks, simulations, and educational applications.
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3. **Advanced Code & Math Generation**
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Produces robust, readable code (Python, JavaScript, C++) with inline reasoning and debugging. Simultaneously capable of solving symbolic math and scientific problems with clarity.
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4. **Structured Output Proficiency**
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Generates content in **Markdown**, **LaTeX**, **JSON**, and **YAML**—tailored for auto-documentation, data structuring, academic formats, and more.
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5. **Multilingual & Multimodal Support**
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Handles technical prompts across **20+ languages** and adapts well to mixed-language code and STEM contexts for a global audience.
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6. **Efficient 1.7B Inference Engine**
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Optimized for performance-to-power ratio—runs smoothly on consumer GPUs (e.g., 8–16GB VRAM), with elite-level results in symbolic tasks.
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---
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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/Capricornus-MoT-1.7B-Supreme1"
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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 = "Explain the code and solve the equation: Write a Python function to solve 2x + 3 = 11, and explain each step."
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messages = [
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{"role": "system", "content": "You are an expert in math, code, and science reasoning."},
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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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---
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## **Intended Use**
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* Symbolic problem-solving in mathematics and science
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* Intelligent code generation, analysis, and debugging
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* Academic research assistants and structured STEM tutors
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* Multilingual, structured output generation for documentation
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* Ideal for developers, educators, and edge deployment in technical domains
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---
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## **Limitations**
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* May not match performance of larger models on long-form generative or creative tasks
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* Context window constraints affect large dataset or document processing
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* Focused on STEM reasoning—free-form dialogue and general conversation are secondary
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* Complex chaining tasks might require manual prompt engineering
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---
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## **References**
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1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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3. [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts)
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4. [Open Code Reasoning Dataset](https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
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