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.

GGUF: https://huggingface.co/prithivMLmods/Capricornus-MoT-1.7B-Supreme1-GGUF

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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.

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Quickstart with Transformers

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)

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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

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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

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References

1. Qwen2.5 Technical Report (2024)
2. YaRN: Efficient Context Window Extension of Large Language Models
3. open-r1/Mixture-of-Thoughts
4. Open Code Reasoning Dataset