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