Improve model card for Value-Guided Search

This PR improves the model card by:

- Adding a description of the model and its purpose.
- Linking to the paper "Value-Guided Search for Efficient Chain-of-Thought Reasoning".
- Adding a link to the official code repository.
- Adding a code snippet for loading and using the model.
- Updating the `pipeline_tag` to `text-generation`.
- Adding the `value-guided-search` tag.

README.md

@@ -1,7 +1,8 @@
 ---
 library_name: transformers
-pipeline_tag: text-classification
-tags: []
+pipeline_tag: text-generation
+tags:
+- value-guided-search
 ---
 
 # Model Card for Model ID
@@ -10,4 +11,32 @@ tags: []
 
 [Value-Guided Search for Efficient Chain-of-Thought Reasoning](https://huggingface.co/papers/2505.17373)
 
-Code: https://github.com/VGS-AI/Value-Guided-Search
+Code: https://github.com/VGS-AI/Value-Guided-Search
+
+This model is a `Qwen2ForClassifier` model, a modified version of the Qwen2 model for classification tasks, which is used to guide chain-of-thought reasoning.
+
+## Usage
+
+To load the model, you can use the following code snippet:
+
+```python
+import classifier_lib
+import torch
+
+model_loading_kwargs = dict(attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False)
+classifier = classifier_lib.Qwen2ForClassifier.from_pretrained("VGS-AI/DeepSeek-VM-1.5B", **model_loading_kwargs)
+```
+
+To apply the model to `input_ids`, you can use the following code snippet:
+
+```python
+import torch
+
+device = torch.device("cuda")
+# your input_ids
+input_ids = torch.tensor([151646, 151644, 18, 13, 47238, ...], dtype=torch.long, device=device)
+attention_mask = torch.ones_like(input_ids)
+classifier_outputs = classifier(input_ids.unsqueeze(0), attention_mask=attention_mask.unsqueeze(0))
+# use last index of the sequence
+scores = classifier_outputs.success_probs.squeeze(0)[-1].item()
+```