Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+  - svector
+  - theta-35-mini
+  - theta
+---
+
+# Theta-35-mini
+
+A distilled, lightweight version of our Theta-35 main model, built on the Qwen architecture and distilled with the GRPO technique for high efficiency and strong performance in a compact footprint.
+
+## Model Description
+
+**Theta-35-mini** is a small-footprint autoregressive language model distilled from our flagship Theta-35 model. We leveraged:
+
+- **Qwen Model Architecture**: Starting from the Qwen2 base, adapting its efficient transformer blocks and optimized attention kernels.
+- **GRPO Distillation**: Guided Representation Projection Optimization (GRPO) to transfer knowledge from Theta-35 to Theta-35-mini, preserving accuracy while drastically reducing parameter count.
+
+This makes Theta-35-mini ideal for on-device inference, low-latency applications, and scenarios with tight compute or memory budgets.
+
+## Intended Uses
+
+- **On-device text generation** (mobile apps, embedded systems)  
+- **Real-time chatbots** and conversational agents  
+- **Edge AI** applications with strict resource constraints
+
+## Usage
+
+```bash
+# Install transformers
+pip install transformers
+
+# Load the model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")
+model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Theta-35-Mini")
+
+# Generate text
+inputs = tokenizer("Once upon a time", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))