File size: 2,067 Bytes
42afa57 c9dd944 f8fb987 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 42afa57 c9dd944 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
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
--- |