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| title: Model Fit Finder | |
| emoji: 👀 | |
| colorFrom: red | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.4.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Space that helps you choose the right type of NLP model | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # Model Fit Finder (CPU) | |
| **Model Fit Finder** is a decision-support Space that helps you choose the **right type of NLP model** and **concrete Hugging Face models** for your task — without training, without GPU, and without guesswork. | |
| The Space is designed to reflect real-world AI engineering decisions rather than showcase a single model demo. | |
| --- | |
| ## What this Space does | |
| The Space guides the user through a small set of practical questions and then: | |
| * identifies the **appropriate model category** (instruction, QA, embeddings), | |
| * ranks and recommends **at least 3 concrete Hugging Face models**, | |
| * explains **why these models were selected**, | |
| * adapts recommendations based on **language, compute budget, and priority**, | |
| * optionally pulls **up-to-date models directly from Hugging Face Hub**. | |
| All recommendations are **CPU-friendly** and suitable for lightweight prototyping and production planning. | |
| --- | |
| ## Supported NLP tasks | |
| The Space currently supports three common NLP problem types: | |
| ### 1. Chat / instruction-following / generation | |
| For tasks such as: | |
| * chatbots | |
| * summarization | |
| * explanation | |
| * instruction-based text processing | |
| Recommended models are **instruction-tuned text-to-text or generative models**. | |
| --- | |
| ### 2. Question Answering from documents (extractive QA) | |
| For tasks where: | |
| * you have a document or text, | |
| * answers must come strictly from that text, | |
| * hallucinations should be minimized. | |
| Recommended models are **extractive QA models** fine-tuned on datasets like SQuAD. | |
| --- | |
| ### 3. Semantic similarity / search / deduplication | |
| For tasks such as: | |
| * finding semantically similar texts, | |
| * detecting near-duplicates, | |
| * semantic search, | |
| * retrieval for RAG pipelines. | |
| Recommended models are **embedding (sentence similarity) models**. | |
| --- | |
| ## How recommendations are generated | |
| Recommendations are **not static**. The Space uses a simple but explicit decision logic based on: | |
| * **Data language** | |
| * EN | |
| * PL | |
| * Mixed / multilingual | |
| * **Compute budget** | |
| * Low (fast, small models) | |
| * Medium (allows larger, higher-quality models) | |
| * **Priority** | |
| * Speed | |
| * Quality | |
| * **Model source** | |
| * Curated (hand-picked, stable baseline) | |
| * HF Live (fresh models from Hugging Face Hub) | |
| * Hybrid (curated + live) | |
| Each candidate model is scored using heuristics such as: | |
| * model size (small vs base), | |
| * language coverage (English vs multilingual), | |
| * suitability for the selected budget and priority, | |
| * stability (curated vs live). | |
| The Space always returns **a minimum of three models**. | |
| --- | |
| ## Hugging Face Live mode | |
| When **HF Live** or **Hybrid** mode is enabled, the Space: | |
| * queries the Hugging Face Hub using task-specific pipeline tags, | |
| * ranks models by popularity (downloads), | |
| * applies language and budget heuristics, | |
| * caches results locally (with TTL), | |
| * allows manual refresh via a **“Refresh HF cache”** button. | |
| This prevents the Space from becoming outdated while keeping results stable and interpretable. | |
| --- | |
| ## What this Space is (and is not) | |
| **This Space is:** | |
| * a model selection assistant, | |
| * a practical decision tool, | |
| * CPU-only and cost-free, | |
| * suitable for engineers, analysts, and ML practitioners. | |
| **This Space is not:** | |
| * a chatbot demo, | |
| * a benchmark leaderboard, | |
| * an automatic “best model” oracle. | |
| Its goal is to help you make **better-informed model choices**, not to hide trade-offs. | |
| --- | |
| ## Example use cases | |
| * *“Which embedding model should I use to detect semantically similar Revit Key Notes?”* | |
| * *“I have a policy document and want reliable question answering without hallucinations.”* | |
| * *“I need a lightweight instruction-following model for short summaries on CPU.”* | |
| * *“Which models make sense for Polish or mixed-language text?”* | |
| --- | |
| ## Technical notes | |
| * No model training is performed. | |
| * No GPU is required. | |
| * All logic runs on CPU. | |
| * Model recommendations are based on metadata, heuristics, and Hugging Face Hub signals. | |
| --- | |
| ## Why this Space exists | |
| Choosing the right model is often harder than using one. | |
| This Space focuses on **model selection reasoning** — the part that usually lives only in engineers’ heads — and makes it explicit, inspectable, and reusable. |