license, language, base_model, pipeline_tag, library_name, tags

license	language	base_model	pipeline_tag	library_name	tags
apache-2.0	en	Qwen/Qwen3-1.7B	text-generation	transformers	trl text-generation-inference math code

Mintaka-Qwen3-1.6B-V3.1

Mintaka-Qwen3-1.6B-V3.1 is a high-efficiency, science-focused reasoning model based on Qwen-1.6B and trained on DeepSeek v3.1 synthetic traces (10,000 entries). It is optimized for random event simulation, logical-problem analysis, and structured scientific reasoning. The model balances symbolic precision with lightweight deployment, making it suitable for researchers, educators, and developers seeking efficient reasoning under constrained compute.

[!note] GGUF: https://huggingface.co/prithivMLmods/Mintaka-Qwen3-1.6B-V3.1-GGUF

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Key Features

1. Scientific Reasoning & Chain-of-Thought Trained on 10,000 synthetic traces from the DeepSeek v3.1 dataset, designed to enhance step-by-step analytical and probabilistic reasoning for simulation tasks and logical puzzles.

2. Advanced Code Reasoning & Generation Supports multi-language coding with explanations, optimization hints, and error detection—useful for algorithm synthesis, debugging, and prototyping.

3. Random Event Simulation & Logical Analysis Tailored for stochastic event simulations, scenario analysis, and formal logical problem solving.

4. Hybrid Symbolic-AI Thinking Combines structured logic, chain-of-thought reasoning, and open-ended inference to deliver robust performance on STEM and simulation tasks.

5. Structured Output Mastery Generates output in LaTeX, Markdown, JSON, CSV, and YAML, suited for technical documentation, experiments, and dataset generation.

6. Optimized Lightweight Footprint for Versatile Deployment Balances performance and efficiency — deployable on mid-range GPUs, offline clusters, and edge AI systems.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Mintaka-Qwen3-1.6B-V3.1"

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

prompt = "Explain the difference between deterministic simulation and stochastic simulation with examples."

messages = [
    {"role": "system", "content": "You are a scientific tutor skilled in reasoning, simulation design, and logical analysis."},
    {"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

• Random event simulation, scenario analysis, and probabilistic reasoning
• Logical-problem analysis and structured scientific tutoring
• Research assistant for physics, computational biology, and interdisciplinary simulation domains
• Structured technical data and experiment result generation
• Deployment in mid-resource environments requiring efficient reasoning

Limitations

• Not tuned for long-form creative writing or conversational small talk
• Context window limitations may hinder multi-document or full codebase analysis
• Optimized specifically for simulation and logical analysis tasks—general chat may underperform
• Prioritizes structured logic and reproducibility over emotional tone