Update README.md

README.md

@@ -1,3 +1,99 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/QwQ-32B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- StreamlinedMemory
+- Reasoning
+- Math
+- Code
+- Qwen
+---
+![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5mIid7-RffKqEeL9t7H2J.png)
+
+# **Sombrero-QwQ-32B-Elite10**  
+
+> Sombrero-QwQ-32B-Elite10 is based on the QwQ 32B modality architecture, optimized for **Streamlined Memory Optimization** while avoiding unwanted textual token mathematical problem-solving and reasoning. This model is tailored for enhanced contextual comprehension, structured text generation, and efficiency in long-context applications.
+
+## **Key Improvements**  
+1. **Optimized Memory Utilization**: Designed to reduce memory overhead while maintaining high-performance inference, making it ideal for complex workflows.  
+2. **Precision in Textual Outputs**: Prioritizes structured content generation and avoids unnecessary mathematical computations in responses.  
+3. **Versatile Adaptability**: Handles diverse queries efficiently, providing coherent and relevant answers across multiple domains.  
+4. **Long-Context Support**: Supports up to 256K tokens for input context and generates up to 16K tokens in a single output, ensuring detailed and structured responses.  
+5. **Multilingual Excellence**: Supports over 35 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.  
+
+## **Quickstart with transformers**  
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Sombrero-QwQ-32B-Elite10"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How does streamlined memory optimization improve AI model efficiency?"
+messages = [
+    {"role": "system", "content": "You are an AI specialized in memory-efficient text generation and structured reasoning."},
+    {"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**  
+1. **Contextual Understanding & Content Generation**:  
+   Designed to generate structured, coherent, and contextually relevant text while minimizing unnecessary computational overhead.  
+
+2. **Enterprise and Research Applications**:  
+   Suitable for large-scale knowledge retrieval, document summarization, and structured data processing.  
+
+3. **Conversational AI & Virtual Assistants**:  
+   Provides human-like conversational experiences while maintaining response clarity and efficiency.  
+
+4. **Multilingual AI Systems**:  
+   Enhances cross-language communication and supports multilingual deployments.  
+
+5. **Long-Form Content Generation**:  
+   Capable of producing extended articles, reports, and structured documents with high coherence.  
+
+## **Limitations**  
+1. **Hardware Requirements**:  
+   Due to its 32B parameter size, high-memory GPUs or TPUs are recommended for optimal performance.  
+
+2. **Avoidance of Mathematical Problem-Solving**:  
+   Unlike traditional AI models, this model is optimized to reduce mathematical computation, which may limit its effectiveness in solving complex numerical problems.  
+
+3. **Potential Bias in Responses**:  
+   While fine-tuned for neutrality, responses may still carry biases from training data.  
+
+4. **Prompt Sensitivity**:  
+   The model’s output quality depends on the structure and clarity of the input prompt.  
+
+5. **Real-Time Awareness Limitations**:  
+   Does not have access to real-world events beyond its training data.