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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dd101bb/gemma-2b-cds", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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Download README.md from dd101bb/gemma-2b-cds: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
-
https://huggingface.co/dd101bb/gemma-2b-cds/resolve/refs%2Fpr%2F2/README.md
- Command line
-
hf download hf://dd101bb/gemma-2b-cds@refs/pr/2/README.md
-
curl -L -o README.md https://huggingface.co/dd101bb/gemma-2b-cds/resolve/refs%2Fpr%2F2/README.md
1.18 kB
metadata
base_model:
- google/gemma-2-2b-it
library_name: transformers
license: mit
pipeline_tag: text-generation
R2ec: Towards Large Recommender Models with Reasoning
R2ec is a large recommender model with reasoning, generating both natural language rationales and ranked item predictions. The model is fine-tuned with reinforcement learning to enhance its reasoning capabilities for more effective recommendations.
Sources
- Repository: https://github.com/YRYangang/RRec
- Paper: https://arxiv.org/abs/2505.16994
Licence
This code repository is licensed under MIT License. The use of R2ec models is also subject to MIT License. R2ec series support commercial use and distillation.
Citation
@misc{you2025r2ec,
title={$\text{R}^2\text{ec}$: Towards Large Recommender Models with Reasoning},
author={Runyang You and Yongqi Li and Xinyu Lin and Xin Zhang and Wenjie Wang and Wenjie Li and Liqiang Nie},
year={2025},
eprint={2505.16994},
archivePrefix={arXiv},
primaryClass={cs.IR}
}