Instructions to use dd101bb/gemma-2b-cds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dd101bb/gemma-2b-cds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dd101bb/gemma-2b-cds")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dd101bb/gemma-2b-cds", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use dd101bb/gemma-2b-cds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dd101bb/gemma-2b-cds" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dd101bb/gemma-2b-cds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dd101bb/gemma-2b-cds
- SGLang
How to use dd101bb/gemma-2b-cds 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 "dd101bb/gemma-2b-cds" \ --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": "dd101bb/gemma-2b-cds", "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 "dd101bb/gemma-2b-cds" \ --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": "dd101bb/gemma-2b-cds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dd101bb/gemma-2b-cds with Docker Model Runner:
docker model run hf.co/dd101bb/gemma-2b-cds
Add text-ranking pipeline tag (#1)
Browse files- Add text-ranking pipeline tag (1b8c3d017cb0d2136134ef537860de7a336a2072)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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library_name: transformers
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license: mit
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base_model:
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- google/gemma-2-2b-it
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# R<sup>2</sup>ec: Towards Large Recommender Models with Reasoning
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R<sup>2</sup>ec is a large recommender model with reasoning, generating both natural language rationales and ranked item predictions.
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The model is fine-tuned with reinforcement learning to enhance its reasoning capabilities for more effective recommendations.
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<a href="https://arxiv.org/pdf/2505.16994"><b>Paper Link</b>👁️</a>
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</p>
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base_model:
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library_name: transformers
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license: mit
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pipeline_tag: text-ranking
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---
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# R<sup>2</sup>ec: Towards Large Recommender Models with Reasoning
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R<sup>2</sup>ec is a large recommender model with reasoning, generating both natural language rationales and ranked item predictions.
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The model is fine-tuned with reinforcement learning to enhance its reasoning capabilities for more effective recommendations.
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<p align="center">
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<a href="https://arxiv.org/pdf/2505.16994"><b>Paper Link</b>👁️</a>
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</p>
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