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VGS-AI/DeepSeek-VM-1.5B
DeepSeek-VM-1.5B is a text generation model from VGS-AI. Use it when you need the model to write or continue text. It is set up for transformers.
1.5B value model for guiding DeepSeek CoT: arxiv.org/abs/2505.17373.
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From the Hugging Face model README
1.5B value model for guiding DeepSeek CoT: arxiv.org/abs/2505.17373.
Value-Guided Search for Efficient Chain-of-Thought Reasoning
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.
To load the model, you can use the following code snippet:
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:
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()