Improve model card: Add pipeline tag, library name, links, and sample usage

This PR enhances the model card for `NingLab/CASLIE-M` by:

* Adding the `pipeline_tag: image-text-to-text` to improve discoverability on the Hub for multimodal tasks.
* Specifying `library_name: transformers` to enable the automated usage widget, as indicated by the model's configuration and the base model's compatibility.
* Providing direct links to the paper ([Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data](https://huggingface.co/papers/2410.17337)), the project page (https://ninglab.github.io/CASLIE/), and the GitHub repository (https://github.com/ninglab/CASLIE).
* Including a "Sample Usage" section with a command-line inference snippet directly from the GitHub README for immediate usability.

These updates provide richer context and improve the overall user experience on the Hugging Face Hub.

README.md

@@ -1,18 +1,38 @@
 ---
-license: cc-by-4.0
-datasets:
-- NingLab/MMECInstruct
 base_model:
 - mistralai/Mistral-7B-Instruct-v0.3
+datasets:
+- NingLab/MMECInstruct
+license: cc-by-4.0
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 # CASLIE-M
 
-This repo contains the models for "Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data"
+This repository contains the CASLIE-M model presented in the paper [Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data](https://huggingface.co/papers/2410.17337).
+
+Project page: https://ninglab.github.io/CASLIE/
+Code: https://github.com/ninglab/CASLIE
 
 ## CASLIE Models
 The CASLIE-M model is instruction-tuned from the medium-size base model [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
 
+## Sample Usage
+
+To conduct inference, run `python inference.py --model_path $model_path --task $task --output_path $output_path`.
+
+`$model_path` is the path of the instruction-tuned model.
+
+`$task` specifies the task to be tested.
+
+`$output_path` specifies the path where you want to save the inference output.
+
+Example:
+```bash
+python inference.py --model_path NingLab/CASLIE-M --task answerability_prediction --output_path ap.json
+```
+
 ## Citation
 ```bibtex
 @article{ling2024captions,