Update README.md

README.md

@@ -46,4 +46,82 @@ library_name: transformers
 tags:
 - text-generation-inference
 - multilingual
----
+---
+
+# **Lambda-Equulei-1.5B-xLingual**
+
+> **Lambda-Equulei-1.5B-xLingual** is a **multilingual conversational model** fine-tuned from **Qwen2-1.5B**, specifically designed for **cross-lingual chat and experimental conversations** across **30+ languages**. It brings advanced multilingual understanding and natural dialogue capabilities in a compact size, ideal for international communication tools, language learning platforms, and global conversational assistants.
+
+## **Key Features**
+1. **Multilingual Conversational Excellence**  
+   Trained to engage in natural, flowing conversations across 30+ languages, Lambda-Equulei-1.5B-xLingual enables seamless cross-cultural communication and supports diverse linguistic contexts for global applications.
+
+2. **Extensive Language Support (30+ Languages)**  
+   Capable of understanding, responding, and maintaining context fluently in **over 30 languages** including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, Hindi, Portuguese, Russian, Italian, Dutch, and many more regional languages.
+
+3. **Compact yet Conversationally Rich**  
+   While only 1.5B parameters, this model delivers strong performance for natural dialogue, context retention, cultural awareness, and nuanced conversations with minimal resource demands.
+
+4. **Experimental Conversational AI**  
+   Provides dynamic, context-aware responses that adapt to different conversational styles, cultural nuances, and communication patterns across languages.
+
+## **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Lambda-Equulei-1.5B-xLingual"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Hello! Can you help me practice Spanish conversation?"
+messages = [
+    {"role": "system", "content": "You are a helpful multilingual assistant capable of conversing naturally in over 30 languages."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## **Intended Use**
+- **Multilingual Chat Applications**: Natural conversation support across 30+ languages for global platforms.
+- **Language Learning Tools**: Interactive practice partners for students learning new languages.
+- **International Customer Support**: Cross-cultural communication for global businesses and services.
+- **Cultural Exchange Platforms**: Facilitating meaningful conversations between speakers of different languages.
+- **Lightweight Multilingual Bots**: Embedded use cases in mobile apps, web platforms, or resource-constrained environments.
+
+## **Limitations**
+1. **Experimental Nature**:  
+   As an experimental conversational model, responses may vary in quality and consistency across different languages and contexts.
+
+2. **Language Proficiency Variation**:  
+   While supporting 30+ languages, proficiency levels may differ between major languages (English, Chinese, Spanish) and less common regional languages.
+
+3. **Parameter Scale Constraints**:  
+   Though efficient, the 1.5B parameter size may limit performance on highly complex multilingual tasks compared to larger models.
+
+4. **Bias from Base Model**:  
+   Inherits any biases from Qwen2-1.5B's pretraining. Cultural sensitivity and output validation recommended for sensitive applications.
+
+5. **Context Length Limitations**:  
+   May struggle with very long conversations or complex multi-turn dialogues requiring extensive context retention.