Update README.md

README.md

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 The *QwQ-LCoT2-7B-Instruct* is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the chain of thought reasoning datasets, focusing on chain-of-thought (CoT) reasoning for problems. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
 
-## Quickstart with Transformers
+# **Quickstart with Transformers**
 
 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
 
@@ -60,4 +60,23 @@ generated_ids = [
 ]
 
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
+```
+
+# **Intended Use**  
+
+The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:  
+
+1. **Instruction Following**: Providing detailed and step-by-step guidance for a wide range of user queries.
+2. **Logical Reasoning**: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
+3. **Text Generation**: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
+4. **Problem-Solving**: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support.
+5. **Knowledge Enhancement**: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
+
+# **Limitations**  
+
+1. **Data Bias**: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
+2. **Context Limitation**: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context.
+3. **Complexity Ceiling**: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs.
+4. **Dependency on Prompt Quality**: The quality and specificity of the user prompt heavily influence the model's responses.
+5. **Non-Factual Outputs**: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics.
+6. **Computational Requirements**: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.