Instructions to use VGS-AI/DeepSeek-VM-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VGS-AI/DeepSeek-VM-1.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VGS-AI/DeepSeek-VM-1.5B")# Load model directly from transformers import AutoTokenizer, Qwen2ForClassifier tokenizer = AutoTokenizer.from_pretrained("VGS-AI/DeepSeek-VM-1.5B") model = Qwen2ForClassifier.from_pretrained("VGS-AI/DeepSeek-VM-1.5B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use VGS-AI/DeepSeek-VM-1.5B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VGS-AI/DeepSeek-VM-1.5B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VGS-AI/DeepSeek-VM-1.5B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VGS-AI/DeepSeek-VM-1.5B
- SGLang
How to use VGS-AI/DeepSeek-VM-1.5B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "VGS-AI/DeepSeek-VM-1.5B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VGS-AI/DeepSeek-VM-1.5B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "VGS-AI/DeepSeek-VM-1.5B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VGS-AI/DeepSeek-VM-1.5B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VGS-AI/DeepSeek-VM-1.5B with Docker Model Runner:
docker model run hf.co/VGS-AI/DeepSeek-VM-1.5B
Improve model card for Value-Guided Search
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by nielsr HF Staff - opened
README.md
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library_name: transformers
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pipeline_tag: text-
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tags:
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---
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# Model Card for Model ID
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[Value-Guided Search for Efficient Chain-of-Thought Reasoning](https://huggingface.co/papers/2505.17373)
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Code: https://github.com/
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---
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- value-guided-search
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---
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# Model Card for Model ID
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[Value-Guided Search for Efficient Chain-of-Thought Reasoning](https://huggingface.co/papers/2505.17373)
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Code: https://github.com/kaiwenw/value-guided-search
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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.
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## Usage
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To load the model, you can use the following code snippet:
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```python
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import classifier_lib
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import torch
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model_loading_kwargs = dict(attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False)
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classifier = classifier_lib.Qwen2ForClassifier.from_pretrained("VGS-AI/DeepSeek-VM-1.5B", **model_loading_kwargs)
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```
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To apply the model to `input_ids`, you can use the following code snippet:
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```python
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import torch
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device = torch.device("cuda")
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# your input_ids
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input_ids = torch.tensor([151646, 151644, 18, 13, 47238, ...], dtype=torch.long, device=device)
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attention_mask = torch.ones_like(input_ids)
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classifier_outputs = classifier(input_ids.unsqueeze(0), attention_mask=attention_mask.unsqueeze(0))
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# use last index of the sequence
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scores = classifier_outputs.success_probs.squeeze(0)[-1].item()
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```
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