Text Generation
Transformers
PyTorch
English
llama
llama-2
self-instruct
distillation
synthetic instruction
text-generation-inference
Instructions to use NousResearch/Nous-Hermes-Llama2-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/Nous-Hermes-Llama2-70b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/Nous-Hermes-Llama2-70b")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b") model = AutoModelForMultimodalLM.from_pretrained("NousResearch/Nous-Hermes-Llama2-70b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NousResearch/Nous-Hermes-Llama2-70b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/Nous-Hermes-Llama2-70b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/Nous-Hermes-Llama2-70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NousResearch/Nous-Hermes-Llama2-70b
- SGLang
How to use NousResearch/Nous-Hermes-Llama2-70b 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 "NousResearch/Nous-Hermes-Llama2-70b" \ --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": "NousResearch/Nous-Hermes-Llama2-70b", "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 "NousResearch/Nous-Hermes-Llama2-70b" \ --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": "NousResearch/Nous-Hermes-Llama2-70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NousResearch/Nous-Hermes-Llama2-70b with Docker Model Runner:
docker model run hf.co/NousResearch/Nous-Hermes-Llama2-70b
- Xet hash:
- ce8fac2d40eb8ab793ef5a5bbfc71e38504cbca717c8da4c188406a8dad80fc3
- Size of remote file:
- 9.85 GB
- SHA256:
- 6dda6b5049bf8ae35d5ca747a098253b95163ad1e80a9dc1ea83a0a25602e318
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