Instructions to use cesun/cbllm-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cesun/cbllm-generation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cesun/cbllm-generation")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cesun/cbllm-generation", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cesun/cbllm-generation with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cesun/cbllm-generation" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cesun/cbllm-generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cesun/cbllm-generation
- SGLang
How to use cesun/cbllm-generation 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 "cesun/cbllm-generation" \ --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": "cesun/cbllm-generation", "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 "cesun/cbllm-generation" \ --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": "cesun/cbllm-generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cesun/cbllm-generation with Docker Model Runner:
docker model run hf.co/cesun/cbllm-generation
Add library name to metadata
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README.md
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pipeline_tag: text-generation
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tags:
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- InterpretableLLMs
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---
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# Concept Bottleneck Large Language Models
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This repository contains the Concept Bottleneck Large Language Model (CB-LLM) presented in [Concept Bottleneck Large Language Models](https://arxiv.org/abs/2412.07992).
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Code: [https://github.com/Trustworthy-ML-Lab/CB-LLMs](https://github.com/Trustworthy-ML-Lab/CB-LLMs)
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This model offers inherent interpretability and controllability in text generation. See the linked paper and GitHub repository for details on training and usage.
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pipeline_tag: text-generation
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tags:
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- InterpretableLLMs
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library_name: transformers
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---
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# Concept Bottleneck Large Language Models
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This repository contains the Concept Bottleneck Large Language Model (CB-LLM) presented in [Concept Bottleneck Large Language Models](https://arxiv.org/abs/2412.07992).
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Code: [https://github.com/Trustworthy-ML-Lab/CB-LLMs](https://github.com/Trustworthy-ML-Lab/CB-LLMs)
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This model offers inherent interpretability and controllability in text generation. See the linked paper and GitHub repository for details on training and usage.
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