---
license: apache-2.0
tags:
- trl
- text-generation-inference
- math
- science
- code
- v3.1
- stem
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
library_name: transformers
---

![12.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bRrbrQsIP7JOye9EW7lfK.png)

# **Capella-Qwen3-DS-V3.1-4B**

> **Capella-Qwen3-DS-V3.1-4B** is a reasoning-focused model fine-tuned on **Qwen-4B** using **DeepSeek v3.1 synthetic traces (10K)**.
> It specializes in **random event simulations**, **logical problem analysis**, and structured reasoning tasks.
> The model blends symbolic precision, probabilistic logic, and structured output fluency—making it an ideal tool for researchers, educators, and developers working with uncertainty modeling and event-driven analysis.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B-GGUF](https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B-GGUF)

---

## **Key Features**

1. **Event Simulation & Logical Analysis**
   Fine-tuned on **10,000 synthetic traces** from DeepSeek v3.1 to model random events, probability-driven reasoning, and logical decision-making.

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

3. **Mathematical & Probabilistic Problem Solving**
   Performs analytical reasoning across probability, statistics, and mathematics—explaining concepts, solving equations, and simulating uncertain outcomes.

4. **Hybrid Symbolic-Probabilistic Thinking**
   Combines structured logic, probabilistic inference, and chain-of-thought reasoning, delivering robust performance on uncertainty-driven tasks.

5. **Structured Output Mastery**
   Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for technical documentation, simulations, and structured analysis.

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

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Capella-Qwen3-DS-V3.1-4B"

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

prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."

messages = [
    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and coding."},
    {"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, probability modeling, and uncertainty analysis
* Logical problem-solving in research and education
* Structured data and technical content generation
* STEM-focused chatbot or API for probabilistic reasoning tools
* Deployment in mid-resource environments requiring efficient reasoning

---

## **Limitations**

* Not tuned for general-purpose or creative writing
* Context limitations may hinder multi-document or full codebase analysis
* Specialized for simulations and logical reasoning—general chat may underperform
* Prioritizes probabilistic and logical precision over casual or emotional tone