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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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- text-generation-inference |
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- StreamlinedMemory |
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- Reasoning |
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--- |
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# **Sombrero-QwQ-32B-Elite10** |
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> 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. |
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## **Key Improvements** |
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1. **Optimized Memory Utilization**: Designed to reduce memory overhead while maintaining high-performance inference, making it ideal for complex workflows. |
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2. **Precision in Textual Outputs**: Prioritizes structured content generation and avoids unnecessary mathematical computations in responses. |
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3. **Versatile Adaptability**: Handles diverse queries efficiently, providing coherent and relevant answers across multiple domains. |
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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. |
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5. **Multilingual Excellence**: Supports over 35 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. |
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## **Quickstart with transformers** |
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Sombrero-QwQ-32B-Elite10" |
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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 = "How does streamlined memory optimization improve AI model efficiency?" |
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messages = [ |
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{"role": "system", "content": "You are an AI specialized in memory-efficient text generation and structured reasoning."}, |
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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. **Contextual Understanding & Content Generation**: |
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Designed to generate structured, coherent, and contextually relevant text while minimizing unnecessary computational overhead. |
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2. **Enterprise and Research Applications**: |
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Suitable for large-scale knowledge retrieval, document summarization, and structured data processing. |
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3. **Conversational AI & Virtual Assistants**: |
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Provides human-like conversational experiences while maintaining response clarity and efficiency. |
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4. **Multilingual AI Systems**: |
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Enhances cross-language communication and supports multilingual deployments. |
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5. **Long-Form Content Generation**: |
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Capable of producing extended articles, reports, and structured documents with high coherence. |
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## **Limitations** |
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1. **Hardware Requirements**: |
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Due to its 32B parameter size, high-memory GPUs or TPUs are recommended for optimal performance. |
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2. **Avoidance of Mathematical Problem-Solving**: |
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Unlike traditional AI models, this model is optimized to reduce mathematical computation, which may limit its effectiveness in solving complex numerical problems. |
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3. **Potential Bias in Responses**: |
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While fine-tuned for neutrality, responses may still carry biases from training data. |
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4. **Prompt Sensitivity**: |
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The model’s output quality depends on the structure and clarity of the input prompt. |
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5. **Real-Time Awareness Limitations**: |
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Does not have access to real-world events beyond its training data. |
