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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/QwQ-32B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- StreamlinedMemory |
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- text-generation-inference |
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--- |
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# **Sombrero-QwQ-32B-Elite11** |
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> 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. |
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## **Key Improvements** |
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1. **Optimized Memory Utilization**: Designed to minimize computational overhead while maintaining high accuracy and response coherence. |
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2. **Advanced Problem-Solving**: Excels in mathematical reasoning, step-by-step solutions, and logical deductions. |
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3. **Superior Coding Capabilities**: Fine-tuned for various programming languages, assisting in debugging, generating code snippets, and optimizing algorithms. |
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4. **Enhanced Explanatory Depth**: Provides structured, well-organized explanations for complex queries across different domains. |
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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. |
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6. **Multilingual Proficiency**: Supports over **35 languages**, including English, Chinese, French, Spanish, German, Russian, Japanese, Arabic, and more. |
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## **Quickstart with Transformers** |
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Here is a code snippet demonstrating how to load the tokenizer and model for streamlined memory-efficient inference: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Sombrero-QwQ-32B-Elite11" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Write an optimized Python function for matrix multiplication." |
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messages = [ |
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{"role": "system", "content": "You are an AI assistant specializing in coding and problem-solving."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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1. **Coding and Development Assistance**: |
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- Generates optimized code snippets for multiple programming languages. |
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- Assists with debugging, refactoring, and explaining algorithms. |
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- Converts pseudocode to functional implementations efficiently. |
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2. **Mathematical and Logical Problem-Solving**: |
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- Excels in step-by-step explanations for complex mathematical problems. |
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- Generates proofs, formulas, and structured reasoning for numerical analysis. |
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3. **Explanatory and Technical Writing**: |
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- Ideal for generating technical documentation, research summaries, and structured reports. |
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- Provides detailed breakdowns of complex topics in an easy-to-understand manner. |
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4. **AI-Powered Conversational Agents**: |
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- Enhances chatbot interactions with **accurate, structured, and contextually relevant** responses. |
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- Adapts to different conversational styles while maintaining coherence. |
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5. **Multilingual Applications**: |
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- Supports multilingual responses for global usability. |
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- Capable of **programming language translations** and **text-to-code conversions**. |
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6. **Long-Form Content Generation**: |
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- Capable of generating **extensive articles, research papers, and code documentation** without losing coherence. |
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## **Limitations** |
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1. **High Computational Requirements**: |
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- Requires high-memory **GPUs or TPUs** for optimal performance, especially with long-context processing. |
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2. **Potential Bias in Outputs**: |
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- Although optimized for neutrality, responses may reflect biases present in training data. |
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3. **Sensitivity to Prompt Engineering**: |
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- The quality of the response depends on how well the input query is structured. |
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4. **Error Accumulation in Large Outputs**: |
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- Minor inconsistencies in early responses can propagate through long-form content. |
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5. **Limited Awareness of Real-Time Data**: |
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- Lacks direct access to **real-time updates, news, or dynamic internet data** beyond its training cutoff. |
