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Model: prithivMLmods/Mintaka-Qwen3-1.6B-V3.1
Source: Original Platform

README.md

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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
+---
+
+![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