prithivMLmods/Sombrero-QwQ-32B-Elite10-Broken

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:

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.