Improve model card: Add metadata, prominent links, and basic usage example

README.md

@@ -1,6 +1,21 @@
 ---
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - test-time-scaling
+  - reflective-model
+  - mathematics
+  - code
+  - reasoning
 ---
+
+# MetaStone-S1: Test-Time Scaling with Reflective Generative Model
+
+**Paper:** [Test-Time Scaling with Reflective Generative Model](https://huggingface.co/papers/2507.01951)
+**Project page:** [wenxiaobai.com](https://www.wenxiaobai.com/)
+**Code:** [MetaStone-AI/MetaStone-S1](https://github.com/MetaStone-AI/MetaStone-S1)
+
 ## Introduction
 We release our first reflective generative model: MetaStone-S1.
 With only 32B parameters, MetaStone-S1 performs comparably to the OpenAI-o3 series on mathematics, coding, and Chinese reasoning tasks.
@@ -12,8 +27,45 @@ By sharing the backbone network between the PRMs and policy models, MetaStone‑
 
 <img src="./figures/intro.jpg" alt="Introduction" width="800">
 
-This repo contains the training and evaluation code of MetaStone-S1. For full details please refer to our [paper](https://arxiv.org/abs/2507.01951) and [our official website](https://www.wenxiaobai.com/).
-
+This repository contains the training and evaluation code for MetaStone-S1. For full details, please refer to our [paper](https://huggingface.co/papers/2507.01951) and [official website](https://www.wenxiaobai.com/).
+
+## Usage
+You can load the model using the `transformers` library for basic text generation.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+# Note: For full functionality of MetaStone-S1's reflective generative capabilities
+# (e.g., using the Process Reward Model for enhanced reasoning modes and test-time scaling),
+# please refer to the official GitHub repository for detailed inference pipeline.
+model_name = "MetaStoneTec/MetaStone-S1-32B" # Use MetaStoneTec/MetaStone-S1-7B or MetaStoneTec/MetaStone-S1-1.5B for other sizes
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16, # Use torch.float16 if bfloat16 is not supported by your GPU
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example text generation
+prompt = "What is the capital of France?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate text
+outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.7)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+
+# Example with a specific prompt format (if applicable, adjust as per model's fine-tuning)
+# For models fine-tuned with specific chat templates, use tokenizer.apply_chat_template:
+# messages = [{"role": "user", "content": "Hello, how are you today?"}]
+# prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+# inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+# outputs = model.generate(**inputs, max_new_tokens=50)
+# generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# print(generated_text)
+```
 
 ## Performance