Update README.md

README.md

@@ -1,3 +1,103 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/QwQ-32B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- StreamlinedMemory
+- text-generation-inference
+- coding
+- Qwen
+---
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YuiCLMX-GldYAxX0NFvAi.png)
+
+# **Sombrero-QwQ-32B-Elite11**  
+
+> Sombrero-QwQ-32B-Elite11 is based on the QwQ 32B architecture by Qwen, optimized for **Streamlined Memory Optimization** and enhanced **explanatory, mathematical problem-solving, and reasoning capabilities**. This model is particularly effective for **coding purposes**, avoiding unwanted textual token generation and ensuring efficiency in structured programming outputs.  
+
+## **Key Improvements**  
+1. **Optimized Memory Utilization**: Designed to minimize computational overhead while maintaining high accuracy and response coherence.  
+2. **Advanced Problem-Solving**: Excels in mathematical reasoning, step-by-step solutions, and logical deductions.  
+3. **Superior Coding Capabilities**: Fine-tuned for various programming languages, assisting in debugging, generating code snippets, and optimizing algorithms.  
+4. **Enhanced Explanatory Depth**: Provides structured, well-organized explanations for complex queries across different domains.  
+5. **Long-Context Processing**: Supports up to **256K tokens** for input and can generate up to **12K tokens** in a single output, making it ideal for extensive documentation and detailed responses.  
+6. **Multilingual Proficiency**: Supports over **35 languages**, including English, Chinese, French, Spanish, German, Russian, Japanese, Arabic, and more.  
+
+## **Quickstart with Transformers**  
+
+Here is a code snippet demonstrating how to load the tokenizer and model for streamlined memory-efficient inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Sombrero-QwQ-32B-Elite11"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write an optimized Python function for matrix multiplication."
+messages = [
+    {"role": "system", "content": "You are an AI assistant specializing in coding and problem-solving."},
+    {"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. **Coding and Development Assistance**:  
+   - Generates optimized code snippets for multiple programming languages.  
+   - Assists with debugging, refactoring, and explaining algorithms.  
+   - Converts pseudocode to functional implementations efficiently.  
+
+2. **Mathematical and Logical Problem-Solving**:  
+   - Excels in step-by-step explanations for complex mathematical problems.  
+   - Generates proofs, formulas, and structured reasoning for numerical analysis.  
+
+3. **Explanatory and Technical Writing**:  
+   - Ideal for generating technical documentation, research summaries, and structured reports.  
+   - Provides detailed breakdowns of complex topics in an easy-to-understand manner.  
+
+4. **AI-Powered Conversational Agents**:  
+   - Enhances chatbot interactions with **accurate, structured, and contextually relevant** responses.  
+   - Adapts to different conversational styles while maintaining coherence.  
+
+5. **Multilingual Applications**:  
+   - Supports multilingual responses for global usability.  
+   - Capable of **programming language translations** and **text-to-code conversions**.  
+
+6. **Long-Form Content Generation**:  
+   - Capable of generating **extensive articles, research papers, and code documentation** without losing coherence.  
+
+## **Limitations**  
+1. **High Computational Requirements**:  
+   - Requires high-memory **GPUs or TPUs** for optimal performance, especially with long-context processing.  
+2. **Potential Bias in Outputs**:  
+   - Although optimized for neutrality, responses may reflect biases present in training data.  
+3. **Sensitivity to Prompt Engineering**:  
+   - The quality of the response depends on how well the input query is structured.  
+4. **Error Accumulation in Large Outputs**:  
+   - Minor inconsistencies in early responses can propagate through long-form content.  
+5. **Limited Awareness of Real-Time Data**:  
+   - Lacks direct access to **real-time updates, news, or dynamic internet data** beyond its training cutoff.