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

![13.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ad3YPieXLAZG6-vBWDnhm.png)

# **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

---

## **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**.

---

## **Quickstart with Transformers**

```python
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)
```

---

## **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