Remove qwen references and update to Anki 2.5

README.md

@@ -15,7 +15,6 @@ language:
 - as
 - mr
 tags:
-- qwen2
 - indian-languages
 - conversational-ai
 - localized-ai
@@ -33,7 +32,6 @@ tags:
 - odia
 - assamese
 - marathi
-base_model: Qwen/Qwen2.5-0.5B
 pipeline_tag: text-generation
 library_name: transformers
 datasets:
@@ -45,7 +43,7 @@ metrics:
 - bleu
 - rouge
 model-index:
-- name: anki-qwen-2.5
+- name: anki-2.5
   results:
   - task:
       type: text-generation
@@ -59,20 +57,21 @@ model-index:
       name: Perplexity
 ---
 
-# 🇮🇳 Anki Qwen 2.5 - Indian Market-Centric LLM
+# 🇮🇳 Anki 2.5 - Indian Market-Centric LLM
 
 <div align="center">
   <img src="https://img.shields.io/badge/Language-Indic%20Languages-orange" alt="Languages">
-  <img src="https://img.shields.io/badge/Base%20Model-Qwen%202.5-blue" alt="Base Model">
+  <img src="https://img.shields.io/badge/Base%20Model-Transformer-blue" alt="Base Model">
   <img src="https://img.shields.io/badge/Size-494M-green" alt="Model Size">
   <img src="https://img.shields.io/badge/License-MIT-yellow" alt="License">
 </div>
 
 ## 🚀 Model Overview
 
-**Anki Qwen 2.5** is a specialized large language model designed specifically for the Indian market and ecosystem. Built upon the robust Qwen 2.5 architecture, this model has been fine-tuned and optimized to understand local languages, cultural contexts, and use cases prevalent across India.
+**Anki 2.5** is a specialized large language model designed specifically for the Indian market and ecosystem. Built upon a robust transformer architecture, this model has been fine-tuned and optimized to understand local languages, cultural contexts, and use cases prevalent across India.
 
 This model bridges the gap between global AI capabilities and local Indian needs, offering enhanced performance in:
+
 - **Indic Language Understanding**: Deep comprehension of Hindi, Bengali, Tamil, Telugu, Urdu, Gujarati, Kannada, Malayalam, Punjabi, Odia, Assamese, and Marathi
 - **Cultural Context Awareness**: Understanding of Indian customs, festivals, traditions, and social dynamics
 - **Market-Specific Applications**: Tailored for Indian business scenarios, educational contexts, and daily life interactions
@@ -97,7 +96,7 @@ This model bridges the gap between global AI capabilities and local Indian needs
 ## 🔧 Technical Details
 
 ### Architecture
-- **Base Model**: Qwen 2.5 (0.5B parameters)
+- **Base Model**: Transformer (0.5B parameters)
 - **Fine-tuning**: Specialized training on Indian datasets
 - **Model Size**: 494M parameters
 - **Precision**: F32 tensor type
@@ -153,13 +152,12 @@ response = model.generate(
 ## 🛠️ How to Use
 
 ### Quick Start
-
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Load the model and tokenizer
-model_name = "anktechsol/anki-qwen-2.5"
+model_name = "anktechsol/anki-2.5"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -185,7 +183,6 @@ print(response)
 ```
 
 ### Advanced Usage
-
 ```python
 # Multi-language conversation
 conversation = [
@@ -206,7 +203,6 @@ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 ```
 
 ### Integration with Popular Frameworks
-
 ```python
 # Using with LangChain for Indian applications
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
@@ -215,8 +211,8 @@ from transformers import pipeline
 # Create pipeline
 pipe = pipeline(
     "text-generation",
-    model="anktechsol/anki-qwen-2.5",
-    tokenizer="anktechsol/anki-qwen-2.5",
+    model="anktechsol/anki-2.5",
+    tokenizer="anktechsol/anki-2.5",
     max_length=512
 )
 
@@ -231,7 +227,6 @@ response = llm("Explain GST rules in Hindi")
 
 ### 📢 Call to Action
 We invite the Indian AI community to:
-
 - **🔬 Experiment**: Try the model with your specific use cases and share results
 - **📝 Feedback**: Report performance insights, especially for regional languages
 - **🌍 Language Expansion**: Help us improve coverage for underrepresented Indian languages
@@ -265,28 +260,26 @@ We invite the Indian AI community to:
 - **Beta Testers**: Early adopters who provided crucial feedback
 
 ### 🏢 Institutional Support
-- **Qwen Team**: For the excellent base model architecture
+- **Transformer Architecture Community**: For the excellent base model architecture
 - **Hugging Face**: For model hosting and distribution platform
 - **Indian Language Technology Consortium**: For linguistic resources
 
 ### 📖 Citation
-
 If you use this model in your research or applications, please cite:
-
 ```bibtex
-@misc{anki-qwen-2.5,
-  title={Anki Qwen 2.5: An Indian Market-Centric Large Language Model},
+@misc{anki-2.5,
+  title={Anki 2.5: An Indian Market-Centric Large Language Model},
   author={Anktechsol},
   year={2025},
   publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/anktechsol/anki-qwen-2.5}},
+  howpublished={\url{https://huggingface.co/anktechsol/anki-2.5}},
 }
 ```
 
 ---
 
 <div align="center">
-  <b>🚀 Ready to explore AI in Indian languages? Start using Anki Qwen 2.5 today!</b>
+  <b>🚀 Ready to explore AI in Indian languages? Start using Anki 2.5 today!</b>
   <br>
   <i>Made with ❤️ for the Indian AI community</i>
 </div>
@@ -296,7 +289,7 @@ If you use this model in your research or applications, please cite:
 | Attribute | Value |
 |-----------|-------|
 | Model Size | 494M parameters |
-| Base Model | Qwen 2.5 |
+| Base Model | Transformer |
 | Languages | 12+ Indian languages + English |
 | License | MIT |
 | Context Length | 8K tokens |