Improve model card: Add metadata, prominent links, and sample usage

README.md

@@ -1,3 +1,13 @@
+---
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# [Test-Time Scaling with Reflective Generative Model](https://huggingface.co/papers/2507.01951)
+
+**Project page:** [https://www.wenxiaobai.com/](https://www.wenxiaobai.com/)
+**Code:** [https://github.com/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.
@@ -11,6 +21,42 @@ By sharing the backbone network between the PRMs and policy models, MetaStone‑
 
 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/).
 
+## Sample Usage
+
+You can easily use MetaStone-S1 for text generation with the `transformers` library by setting `trust_remote_code=True`.
+For full details on using the reflective generative model with its advanced features (SPRM inference, training, etc.), please refer to the [official GitHub repository](https://github.com/MetaStone-AI/MetaStone-S1).
+
+```python
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "MetaStoneTec/MetaStone-S1-1.5B" # Or MetaStoneTec/MetaStone-S1-7B, MetaStoneTec/MetaStone-S1-32B
+pipe = pipeline(
+    "text-generation",
+    model=model_name,
+    tokenizer=AutoTokenizer.from_pretrained(model_name, trust_remote_code=True),
+    torch_dtype=torch.bfloat16, # or torch.float16 depending on your hardware
+    device_map="auto",
+    trust_remote_code=True, # Required for models with custom architectures like Qwen2
+)
+
+# Example: Text Generation
+input_text = "The key to life is"
+generated_text = pipe(input_text, max_new_tokens=20, do_sample=True)[0]["generated_text"]
+print(f"Input: {input_text}
+Output: {generated_text}")
+
+# Example: Using chat template for conversational models
+# Note: Ensure the tokenizer for the specific model has a chat template configured.
+# You might need to load the model and tokenizer separately for chat templates.
+# tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+# model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
+# messages = [{"role": "user", "content": "Hi! How are you?"}]
+# text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+# inputs = tokenizer(text, return_tensors="pt").to(model.device)
+# outputs = model.generate(inputs.input_ids, max_new_tokens=30)
+# print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Performance