---
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.