🚀 Zira-Z.1 🌟

The Bilingual Beast Built on Qwen 2.5 (7B)

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🧠 Model Highlights

Zira-Z.1 isn't just a model — it's a revolution in understanding both English and Hinglish.
Born from the powerful DNA of Qwen 2.5 (7B), this multilingual marvel was fine-tuned for raw text generation across two of the most widely spoken languages in the world.

• 💥 Base: Qwen 2.5 - 7B (One of the finest open LLMs out there)
• 🗣️ Languages: English 🇬🇧 + Hinglish 🇮🇳 (Code-mixed, no pure Hindi)
• 🔧 Training: Fine-tuned on diverse bilingual corpora — clean, simple text format (non-instruct)
• 🦾 Purpose: General-purpose text generation, especially where English and Hinglish blend naturally

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.

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🔍 Why Zira-Z.1?

Because multilingual LLMs are cool.
But Zira-Z.1 is cooler. 😎

• 🔗 Code-switching? Natural.
• ✍️ Generates culturally fluent, relatable Hinglish.
• 📚 Handles casual text, commentary, social chatter, and more.
• 🎯 Perfect for early-stage Indic bilingual applications and experimentation

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📉 Training Curve

She trained hard, and it shows...

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🛠️ Usage

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))'

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🧬 License & Contribution

• 📜 License: Open for research & commercial use (see LICENSE)
• 🤝 Contributions: Welcomed with open arms (and open pull requests)

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