license, datasets, language, base_model, pipeline_tag, library_name, tags

license	datasets	language	base_model	pipeline_tag	library_name	tags
apache-2.0	open-r1/Mixture-of-Thoughts	en	Qwen/Qwen3-0.6B	text-generation	transformers	text-generation-inference code moe math stem trl

Nenque-MoT-0.6B-Elite14

Nenque-MoT-0.6B-Elite14 is a compact, high-efficiency model tailored for mathematical reasoning, code generation, and structured technical inference. Fine-tuned from Qwen3-0.6B using the MoT (Mixture of Thoughts) dataset—with a focus on math expert clusters—this model delivers strong symbolic performance in low-resource environments. Despite its 0.6B parameter size, it offers elite-level precision across STEM and multilingual technical domains.

Note

GGUF: https://huggingface.co/prithivMLmods/Nenque-MoT-0.6B-Elite14-GGUF

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Key Features

1. MoT Fine-Tuning on Math Expert Clusters Trained on a curated Mixture of Thoughts (MoT) dataset emphasizing symbolic mathematics, code reasoning, and problem-solving, enhancing precision in structured tasks.

2. Elite Mathematical Reasoning Excels in solving algebraic equations, calculus, and symbolic logic step-by-step—suitable for education, competitions, and STEM support tools.

3. Compact Code Assistant Generates concise, explainable code in Python, JavaScript, and others—ideal for code tutoring, bug diagnosis, and fast prototyping.

4. Structured Output Generation Supports generation in Markdown, JSON, LaTeX, and tabular formats, making it a valuable tool for documentation and technical data generation.

5. Multilingual Technical Mastery Delivers consistent results across 20+ languages for math and code—serving global academic and development use cases.

6. Lightweight Inference-Ready Design Optimized for edge devices, GPUs with limited VRAM, and offline deployments, enabling high-quality results on constrained systems.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Nenque-MoT-0.6B-Elite14"

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

prompt = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."

messages = [
    {"role": "system", "content": "You are a step-by-step math tutor."},
    {"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

• Step-by-step mathematical reasoning and symbolic computation
• Lightweight multilingual code generation and debugging
• Structured content generation (e.g., LaTeX, JSON, Markdown)
• Academic tutoring and technical assistant roles
• Deployment in resource-constrained or edge scenarios

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Limitations

• Not suitable for extended creative generation or conversational fluency
• Limited context length impacts performance on long multi-step tasks
• Fine-tuned on technical domains—general chat or abstract logic tasks may underperform
• Specialized for structured outputs—free-form generation is not its focus

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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