Update README.md

README.md

@@ -52,4 +52,54 @@ generated_ids = [
 ]
 
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
+```
+
+# **Intended Use:**
+
+1. **Multilingual Dialogue Systems:**
+   - Designed for conversational AI applications, capable of handling dialogue across multiple languages.
+   - Useful in customer service, chatbots, and other dialogue-centric use cases.
+
+2. **Reasoning and QWQ Dataset Applications:**
+   - Optimized for tasks requiring logical reasoning and contextual understanding, particularly in synthetic datasets like QWQ.
+
+3. **Agentic Retrieval:**
+   - Supports retrieval-augmented generation tasks, helping systems fetch and synthesize information effectively.
+
+4. **Summarization Tasks:**
+   - Excels in summarizing long or complex text while maintaining coherence and relevance.
+
+5. **Instruction-Following Tasks:**
+   - Can execute tasks based on specific user instructions due to instruction-tuning during training.
+
+6. **Language Generation:**
+   - Suitable for generating coherent and contextually relevant text in various domains and styles.
+
+# **Limitations:**
+
+1. **Synthetic Dataset Bias:**
+   - Optimization for QWQ and similar datasets may make the model less effective on real-world or less structured data.
+
+2. **Data Dependency:**
+   - Performance may degrade on tasks or languages not well-represented in the training dataset.
+
+3. **Computational Requirements:**
+   - The optimized transformer architecture may demand significant computational resources, especially for fine-tuning or large-scale deployments.
+
+4. **Potential Hallucinations:**
+   - Like most auto-regressive models, it may generate plausible-sounding but factually incorrect or nonsensical outputs.
+
+5. **RLHF-Specific Biases:**
+   - Reinforcement Learning with Human Feedback (RLHF) can introduce biases based on the preferences of the annotators involved in the feedback process.
+
+6. **Limited Domain Adaptability:**
+   - While effective in reasoning and dialogue tasks, it may struggle with highly specialized domains or out-of-distribution tasks.
+
+7. **Multilingual Limitations:**
+   - Although optimized for multilingual use, certain low-resource languages may exhibit poorer performance compared to high-resource ones.
+
+8. **Ethical Concerns:**
+   - May inadvertently generate inappropriate or harmful content if safeguards are not applied, particularly in sensitive applications.
+
+9. **Real-Time Usability:**
+   - Latency in inference time could limit its effectiveness in real-time applications or when scaling to large user bases.