| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - value-guided-search | |
| # Model Card for Model ID | |
| 1.5B value model for guiding DeepSeek CoT: arxiv.org/abs/2505.17373. | |
| [Value-Guided Search for Efficient Chain-of-Thought Reasoning](https://huggingface.co/papers/2505.17373) | |
| Code: https://github.com/kaiwenw/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() | |
| ``` |