Zira Z.1

🚀 Zira-Z.1 🌟

The Bilingual Beast Built on Qwen 2.5 (7B)

Zira-Z.1 is a revolutionary model that excels in understanding both English and Hinglish. Built upon the powerful Qwen 2.5 (7B), it's fine - tuned for text generation in these two widely - spoken languages.

🚀 Quick Start

To start using Zira-Z.1, you can follow the code example below:

import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HyperX-Sen/Zira-Z.1")
model = AutoModelForCausalLM.from_pretrained("HyperX-Sen/Zira-Z.1")

inputs = tokenizer("Tum kya soch rahe ho about AI?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✨ Features

• Base Model: Qwen 2.5 - 7B, one of the top - notch open LLMs.
• Languages Supported: English and Hinglish (code - mixed, no pure Hindi).
• Training Approach: Fine - tuned on diverse bilingual corpora in a clean, simple text format (non - instruct).
• Purpose: General - purpose text generation, especially suitable for scenarios where English and Hinglish blend naturally.
• Code - switching: It can perform code - switching naturally.
• Hinglish Generation: Generates culturally fluent and relatable Hinglish.
• Text Handling: Capable of handling casual text, commentary, social chatter, etc.
• Application Suitability: Ideal for early - stage Indic bilingual applications and experimentation.

📚 Documentation

Please note that this is a basic text generation model and lacks coherence in its output. The release of the new instruct model has been delayed due to resource constraints, with an expected launch in approximately 5 days.

📉 Training Curve

📄 License

• License: Open for research & commercial use (see LICENSE).
• Contributions: Contributions are welcomed with open arms (and open pull requests).

Made with ❤️, logic, and a lot of chai ☕