---
license: mit
tags:
  - svector
  - theta-35-mini
  - theta
---

# Theta-35-mini

**A lightweight, high-efficiency reasoning model distilled from Theta-35.**
**Theta-35-Mini** is a compact 3B parameter language model developed by **SVECTOR**, built on the Qwen architecture and trained using **Group Relative Policy Optimization (GRPO)**. It is the smaller sibling of our flagship **Theta-35** model (33B parameters), offering efficient performance for resource-constrained environments.

---

## 🔍 Overview

- **Architecture**: Based on Qwen2-style transformer blocks
- **Training Objective**: Autoregressive next-token prediction
- **Technique**: Trained using **Group Relative Policy Optimization (GRPO)** – a reinforcement learning optimization strategy enabling fine-grained control and alignment
- **Size**: 3 billion parameters
- **Parent Model**: [Theta-35 (33B)](https://huggingface.co/SVECTOR-CORPORATION/Theta-35)


## 🚀 Model Highlights

- ✅ **Compact and Capable**: Achieves strong performance despite its small size
- ⚙️ **GRPO-trained**: Trained with Group Relative Policy Optimization for better alignment, coherence, and efficiency
- 💡 **Low-latency Inference**: Ideal for edge and on-device applications
## 📦 How to Use

Install dependencies:

```bash
pip install transformers
```

Run model in Python:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")

# Prompt input
inputs = tokenizer("Once upon a time", return_tensors="pt")

# Generate output
outputs = model.generate(**inputs, max_length=100, temperature=0.7)

# Decode and print
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 📄 License

This model is released under the **MIT License**.

---

## 🏢 About SVECTOR

🔗 Visit us at [svector.co.in](https://www.svector.co.in)

---

## 🙌 Acknowledgements

- DeepSeek GRPO Paper
- Qwen2 Architecture
---