---
license: apache-2.0
language:
- en
base_model:
- Qwen/QwQ-32B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- StreamlinedMemory
- Reasoning
---
![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.