Update README.md

README.md

@@ -7,47 +7,86 @@ tags:
 model-index:
 - name: intent_model
   results: []
+language:
+- en
 ---
+# prompt-intent-mini
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+**A lightweight model for classifying the intent behind a prompt.**  
+Built by [TinyModels](https://huggingface.co/TinyModels) — small footprint, serious performance.
 
-# intent_model
+---
+
+## What it does
+
+`prompt-intent-mini` takes a user prompt as input and returns its intent label — what the user is *actually trying to do* with that prompt. Whether it's a question, a command, a creative request, or something else entirely, the model picks it up fast and with minimal compute.
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+This is useful anywhere you need to route, filter, or understand prompts before passing them downstream — chatbots, pipelines, safety layers, you name it.
 
-## Model description
+---
 
-More information needed
+## Quick Start
 
-## Intended uses & limitations
+```python
+from transformers import pipeline
 
-More information needed
+classifier = pipeline("text-classification", model="TinyModels/prompt-intent-mini")
 
-## Training and evaluation data
+result = classifier("Write me a poem about the ocean")
+print(result)
+# [{'label': 'creative_generation', 'score': 0.97}]
+```
 
-More information needed
+---
 
-## Training procedure
+## Model Details
 
-### Training hyperparameters
+| Property | Value |
+|---|---|
+| **Model type** | Text classification |
+| **Base architecture** | Transformer (encoder) |
+| **Task** | Prompt intent classification |
+| **Size** | Tiny / <50M params |
+| **Framework** | PyTorch + 🤗 Transformers |
+| **Organization** | [TinyModels](https://huggingface.co/TinyModels) |
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
+---
+
+## Why we built this
+
+Most intent classifiers are either too big to run cheaply or too narrow to generalize. We wanted something that sits at the front of a pipeline and just *works* — fast inference, low memory, no GPU required for most use cases.
+
+`prompt-intent-mini` follows the same principle as everything we ship at TinyModels: it does one job well, fits anywhere, and doesn't cost you a GPU bill to run.
+
+---
 
-### Training results
+## Installation
 
+```bash
+pip install transformers torch
+```
+
+---
+
+## Intended Use
+
+- Prompt routing in LLM pipelines
+- Intent-aware moderation or filtering
+- Chatbot understanding layers
+- Any application that needs to know *what a user wants* before acting on it
+
+---
+
+## Limitations
+
+- Trained on English prompts. Other languages may see reduced accuracy.
+- Edge cases with highly ambiguous or mixed-intent prompts may not classify cleanly.
+- This is a `mini` model — for more complex, multi-label scenarios, a larger variant may be more appropriate.
+
+---
 
+## Part of the TinyModels family
 
-### Framework versions
+This model was created by [TinyModels](https://huggingface.co/TinyModels), a community building small, fast, open models that anyone can run. No paywalls. No gatekeeping.
 
-- Transformers 5.0.0
-- Pytorch 2.10.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.2
+> *Tiny models. Huge ambitions.*