Text Generation
Transformers
PyTorch
English
llama
Human Preference Alignment
large language models
text-generation-inference
Instructions to use MetaAligner/MetaAligner-HH-RLHF-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MetaAligner/MetaAligner-HH-RLHF-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MetaAligner/MetaAligner-HH-RLHF-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MetaAligner/MetaAligner-HH-RLHF-7B") model = AutoModelForCausalLM.from_pretrained("MetaAligner/MetaAligner-HH-RLHF-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MetaAligner/MetaAligner-HH-RLHF-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MetaAligner/MetaAligner-HH-RLHF-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaAligner/MetaAligner-HH-RLHF-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MetaAligner/MetaAligner-HH-RLHF-7B
- SGLang
How to use MetaAligner/MetaAligner-HH-RLHF-7B 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 "MetaAligner/MetaAligner-HH-RLHF-7B" \ --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": "MetaAligner/MetaAligner-HH-RLHF-7B", "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 "MetaAligner/MetaAligner-HH-RLHF-7B" \ --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": "MetaAligner/MetaAligner-HH-RLHF-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MetaAligner/MetaAligner-HH-RLHF-7B with Docker Model Runner:
docker model run hf.co/MetaAligner/MetaAligner-HH-RLHF-7B
Commit History
-
Upload 3 files
Verified signaturec769ffa -
Upload 8 files
Verified signatured6ef0ef -
Delete tokenizer_config.json
Verified signatureffa2d81 -
Delete tokenizer.model
Verified signature17fc869 -
Delete tokenizer.json
Verified signaturee857f10 -
Delete special_tokens_map.json
Verified signature15744a4 -
Delete pytorch_model.bin.index.json
Verified signatureed2ee96 -
Delete pytorch_model-00003-of-00003.bin
Verified signature0d0406d -
Delete pytorch_model-00002-of-00003.bin
Verified signature177f81b -
Delete pytorch_model-00001-of-00003.bin
Verified signature77708a1 -
Delete generation_config.json
Verified signatured412100 -
Delete config.json
Verified signature3f1c1c6 -
Delete added_tokens.json
Verified signatureb3c053a
-
Update README.md
a295e5a -
Update README.md
Verified signature245f7a5
-
Update README.md
Verified signaturebf54e95
-
Update README.md
Verified signature6dccdf8 -
Update README.md
Verified signatureafd21bd -
Update README.md
Verified signature65e1664 -
Update README.md
Verified signature3401e6e -
Upload 3 files
Verified signaturee8eb862 -
Upload 8 files
Verified signatureabed1a8 -
initial commit
Verified signatureee14e69