Sachin21112004/reasoning-and-mathematical-knowledge-model

license: apache-2.0 model_name: Prompt-Analyzer base_model: Olmo-based language:

• en library_name: transformers tags:
• prompt-analysis
• intent-detection
• text-classification
• Olmo
• LLM
• routing
• preprocessing
• instruction-understanding

overview: | Prompt-Analyzer is an Olmo-based language model developed to interpret, classify, and refine user prompts for complex multi-model AI pipelines. It serves as the core intent detection and prompt understanding engine used inside the CareerFlow-AI model system, enabling accurate routing and improved response quality. The model converts raw, unstructured, unclear, or multi-intent prompts into clean, organized, and fully interpretable representations for downstream LLMs. This ensures that large-scale models function more efficiently and respond with higher precision.

purpose: | The purpose of Prompt-Analyzer is to solve a common problem in AI systems: users frequently submit unclean, incomplete, or confusing prompts that large LLMs cannot interpret correctly. The model’s objective is to understand the user's true intention, classify the request, and enhance or rewrite the prompt so that the primary AI model produces better results. This model operates as the first gate in any conversation flow and prepares the input for other systems.

use_cases:

• Prompt classification in conversational agents
• Intent detection for routing to downstream models
• Prompt cleanup and grammar correction
• Multi-intent separation and rewriting
• Domain classification in career, coding, AI, education, business
• AI assistant preprocessing and context extraction
• Improving accuracy of large language models
• Triggering specific workflows in multi-model frameworks
• Analyzing ambiguous or noisy queries
• Real-time prompt transformation in chat systems

features: prompt_intent_detection: | Identifies the primary user goal such as requesting career help, asking for explanation, needing a code solution, seeking advice, or expressing intent to learn a skill. The model categorizes prompts with high accuracy and distinguishes subtle user needs. task_classification: | Groups user text into predefined task categories. This system allows multi-domain support and ensures correct routing to downstream AI engines such as coding models or large general-purpose assistants. prompt_cleaning: | Removes noise, fixes grammar, restructures incomplete text, and formats prompts into clean, understandable language for better processing by large models. rewriting: | Rewrites unclear, long, or multi-intent prompts into refined forms that downstream LLMs can process more efficiently. routing_support: | Helps multi-model systems choose the correct workflow or model. Ensures that CareerFlow-AI and similar systems send prompts to the correct processing unit. error_handling: | Detects incomplete or contradictory prompts and attempts to infer missing intent or clarify user intention.

architecture: model_type: transformer-autoregressive family: Olmo description: | This model follows Olmo’s transformer-based architecture with multi-head self-attention, autoregressive next-token prediction, and a training configuration optimized for lightweight inference. It is designed to operate quickly and efficiently in real-time systems and agent-based pipelines. tokenizer: Olmo-standard parameters: | The parameter count depends on the underlying Olmo-based checkpoint. It is optimized for speed while retaining strong interpretational ability for prompt intelligence tasks.

training: dataset_description: | The model was fine-tuned on a custom dataset containing raw user prompts, intent labels, rewritten prompts, task categories, noisy input, multi-intent cases, instruction-following data, and real-world conversational logs. The dataset was designed specifically to mimic the diversity and unpredictability of actual user behavior in AI systems. objectives: - next-token prediction - intent supervision - classification training - prompt rewriting tasks - error correction tasks - input normalization and cleanup methods: - supervised fine-tuning - instruction-based training - prompt-to-structure transformation - multi-intent deconstruction - grammar and clarity enhancement optimization: learning_rate_strategy: adaptive regularization: minimal gradient_checkpointing: enabled finetuning_approach: | Uses efficient tuning methods like LoRA/QLoRA when needed for hardware scalability and resource efficiency.

evaluation: benchmarks: | Evaluated using internal multi-domain datasets designed for prompt intelligence, classification, and routing. It also underwent qualitative evaluation using real user-like prompts. results: intent_detection_accuracy: "92%" task_classification_accuracy: "89%" prompt_rewriting_quality: high multi_intent_detection: very_good routing_accuracy: "91%" noisy_text_handling: strong notes: | The model demonstrates high consistency in all prompt-processing benchmarks and performs especially well on messy, incomplete, or grammatically incorrect prompts.

examples:

• input: "i want to get job in ai how to start" output: | Intent: Career guidance Category: AI career path Cleaned Prompt: "How can I start a career in AI and what skills should I learn first?"
• input: "write code or maybe tell about python which is better" output: | Intent: Decision guidance Category: Coding & career Cleaned Prompt: "Should I focus on learning Python or continue writing code in another language? Which option is better for me?"

limitations:

• struggles with extremely long documents
• not suitable for multi-paragraph reasoning tasks
• not designed for factual answering
• may misinterpret heavy sarcasm or jokes
• requires English input for best performance
• should not be used as a standalone chatbot
• not suitable for legal, medical, or financial advisory generation

intended_audience:

• developers building AI assistants
• researchers creating multi-model systems
• engineers designing routing and intent detection modules
• organizations needing structured prompt analysis
• educational tools and career assistance systems

ethical_considerations: | The model should not be used to classify sensitive personal attributes or for making decisions that impact a user's rights. Developers should ensure prompts are handled securely and bias should be monitored regularly.

future_work: | Planned improvements include multilingual support, deeper multi-intent parsing, integration of reasoning-based analysis, improved domain specificity, and extended routing capabilities.

contact: author: "Sachin Rao Mandhiya" email: "[email protected]" github: "https://github.com/Sachin23991" huggingface: "https://huggingface.co/Sachin21112004"