Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Phi-4-QwQ`](https://huggingface.co/prithivMLmods/Phi-4-QwQ) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/Phi-4-QwQ) for more details on the model.
 
+---
+[Phi-4-QwQ finetuned] from Microsoft's Phi-4 is a state-of-the-art open model developed with a focus on responsible problem solving and advanced reasoning capabilities. Built upon a diverse blend of synthetic datasets, carefully filtered public domain websites, and high-quality academic books and Q&A datasets, Phi-4-QwQ ensures that small, capable models are trained with datasets of exceptional depth and precision.
+
+Phi-4-QwQ adopts a robust safety post-training approach using open-source and in-house synthetic datasets. This involves a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization) techniques, ensuring helpful and harmless outputs across various safety categories.
+Dataset Info
+
+Phi-4-QwQ is fine-tuned on a carefully curated synthetic dataset generated using an advanced pipeline optimized for Chain of Thought (CoT) reasoning and Responsible Problem Breakdown (RPB) methodologies. This ensures that the model excels at:
+
+    Logical reasoning
+    Step-by-step problem-solving
+    Breaking down complex tasks into manageable parts
+
+The dataset also emphasizes responsible decision-making and fairness in generating solutions.
+Run with Transformers
+
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-QwQ")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/Phi-4-QwQ",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+input_text = "Explain the concept of black holes."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=64)
+print(tokenizer.decode(outputs[0]))
+
+For chat-style interactions, use tokenizer.apply_chat_template:
+
+messages = [
+    {"role": "user", "content": "Explain the concept of black holes."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+
+Intended Use
+
+Phi-4-QwQ is tailored for a wide range of applications, especially those involving advanced reasoning, multilingual capabilities, and responsible problem-solving. Its primary use cases include:
+
+    Responsible Problem Solving
+        Breaking down complex problems into logical, actionable steps.
+        Offering ethical, well-rounded solutions in academic and professional contexts.
+
+    Advanced Reasoning Tasks
+        Excelling in mathematics, logic, and scientific reasoning.
+        Providing detailed explanations and systematic answers.
+
+    Content Generation
+        Assisting in generating high-quality content for various domains, including creative writing and technical documentation.
+        Supporting marketers, writers, and educators with detailed and well-structured outputs.
+
+    Educational Support
+        Acting as a virtual tutor for students by generating practice questions, answers, and detailed explanations.
+        Helping educators design learning material that promotes critical thinking and step-by-step problem-solving.
+
+    Customer Support & Dialogue Systems
+        Enabling chatbots and virtual assistants to provide accurate, helpful, and responsible responses.
+        Enhancing customer service with reasoning-driven automation.
+
+    Multilingual Capabilities
+        Supporting multilingual communication and content generation while maintaining contextual accuracy.
+        Assisting in translations with a focus on retaining meaning and nuance.
+
+    Safety-Critical Applications
+        Ensuring safe and harmless outputs, making it suitable for sensitive domains.
+        Providing aligned interactions with human oversight for critical systems.
+
+Limitations
+
+Despite its strengths, Phi-4-QwQ has some limitations that users should be aware of:
+
+    Bias and Fairness
+        While great effort has been made to minimize biases, users should critically assess the model’s output in sensitive scenarios to avoid unintended bias.
+
+    Contextual Interpretation
+        The model may occasionally misinterpret highly nuanced prompts or ambiguous contexts, leading to suboptimal responses.
+
+    Knowledge Cutoff
+        Phi-4-QwQ’s knowledge is static and based on the data available at the time of training. It does not include real-time updates or information on recent developments.
+
+    Safety and Harmlessness
+        Despite post-training safety alignment, inappropriate or harmful outputs may still occur. Continuous monitoring and human oversight are advised when using the model in critical contexts.
+
+    Computational Requirements
+        Deploying Phi-4-QwQ efficiently may require substantial computational resources, particularly for large-scale deployments or real-time applications.
+
+    Ethical Considerations
+        Users are responsible for ensuring that the model is not employed for malicious purposes, such as spreading misinformation, generating harmful content, or facilitating unethical behavior.
+
+    Domain-Specific Expertise
+        While the model is versatile, it may not perform optimally in highly specialized domains (e.g., law, medicine, finance) without further domain-specific fine-tuning.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)