Instructions to use LLM360/AmberSafe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM360/AmberSafe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM360/AmberSafe")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LLM360/AmberSafe") model = AutoModelForCausalLM.from_pretrained("LLM360/AmberSafe") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM360/AmberSafe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM360/AmberSafe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM360/AmberSafe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM360/AmberSafe
- SGLang
How to use LLM360/AmberSafe 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 "LLM360/AmberSafe" \ --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": "LLM360/AmberSafe", "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 "LLM360/AmberSafe" \ --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": "LLM360/AmberSafe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM360/AmberSafe with Docker Model Runner:
docker model run hf.co/LLM360/AmberSafe
Update README.md
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README.md
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## Method
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We followed the instructions in the [dpo repo](https://github.com/eric-mitchell/direct-preference-optimization) to finetune this model.
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| [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) | 330k | cc-by-nc-4.0 |
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## Data Preprocessing
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We filtered the dataset by selecting all data samples with different boolean values in `is_response_0_safe` and `is_response_1_safe`. This would make sure that for each pair in the preference dataset, the chosen text is safe and the rejected one is unsafe.
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## Method
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We followed the instructions in the [dpo repo](https://github.com/eric-mitchell/direct-preference-optimization) to finetune this model.
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