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---
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
---
# **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 |