Improve language tag

README.md

@@ -1,78 +1,89 @@
----
-license: apache-2.0
-tags:
-- trl
-- sft
-- hinglish
-language:
-- en
-- hi
-base_model:
-- Qwen/Qwen2.5-7B
-pipeline_tag: text-generation
----
-# 🚀 Zira-Z.1 🌟  
-### *The Bilingual Beast Built on Qwen 2.5 (7B)*
-
-![Zira-Z.1 Banner](img/banner.png) <!-- Add an epic banner image -->
-
----
-
-## 🧠 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.**
-
----
-
-## 🔍 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
-
----
-
-## 📉 Training Curve
-
-> *She trained hard, and it shows...*
-
-![Insert loss curve here](img/Figure_1.png) <!-- Add your actual training curve image here -->
-
----
-
-## 🛠️ Usage
-
-```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))'
-```
-
----
-
-## 🧬 License & Contribution
-
-- 📜 **License**: Open for research & commercial use (see LICENSE)
-- 🤝 Contributions: Welcomed with open arms (and open pull requests)
-
----
-
+---
+license: apache-2.0
+tags:
+- trl
+- sft
+- hinglish
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B
+pipeline_tag: text-generation
+---
+# 🚀 Zira-Z.1 🌟  
+### *The Bilingual Beast Built on Qwen 2.5 (7B)*
+
+![Zira-Z.1 Banner](img/banner.png) <!-- Add an epic banner image -->
+
+---
+
+## 🧠 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.**
+
+---
+
+## 🔍 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
+
+---
+
+## 📉 Training Curve
+
+> *She trained hard, and it shows...*
+
+![Insert loss curve here](img/Figure_1.png) <!-- Add your actual training curve image here -->
+
+---
+
+## 🛠️ Usage
+
+```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))'
+```
+
+---
+
+## 🧬 License & Contribution
+
+- 📜 **License**: Open for research & commercial use (see LICENSE)
+- 🤝 Contributions: Welcomed with open arms (and open pull requests)
+
+---
+
 Made with ❤️, logic, and a lot of chai ☕