README.md

metadata

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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?”

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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.

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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.