Instructions to use TheFloat16/Llama3-70b-Instruct-TRTLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheFloat16/Llama3-70b-Instruct-TRTLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheFloat16/Llama3-70b-Instruct-TRTLLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheFloat16/Llama3-70b-Instruct-TRTLLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TheFloat16/Llama3-70b-Instruct-TRTLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheFloat16/Llama3-70b-Instruct-TRTLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheFloat16/Llama3-70b-Instruct-TRTLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheFloat16/Llama3-70b-Instruct-TRTLLM
- SGLang
How to use TheFloat16/Llama3-70b-Instruct-TRTLLM 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 "TheFloat16/Llama3-70b-Instruct-TRTLLM" \ --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": "TheFloat16/Llama3-70b-Instruct-TRTLLM", "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 "TheFloat16/Llama3-70b-Instruct-TRTLLM" \ --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": "TheFloat16/Llama3-70b-Instruct-TRTLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheFloat16/Llama3-70b-Instruct-TRTLLM with Docker Model Runner:
docker model run hf.co/TheFloat16/Llama3-70b-Instruct-TRTLLM
Ctrl+K
This model has 45 files scanned as unsafe.
- quantized-llama-3-70b-pp1-tp1-awq-w4a16-kvfp16-gs64
- quantized-llama-3-70b-pp1-tp1-awq-w4a16-kvfp8-gs64
- quantized-llama-3-70b-pp1-tp1-awq-w4a16-kvint8-gs64
- quantized-llama-3-70b-pp1-tp2-awq-w4a16-kvfp16-gs64
- quantized-llama-3-70b-pp1-tp2-awq-w4a16-kvfp8-gs64
- quantized-llama-3-70b-pp1-tp2-awq-w4a16-kvint8-gs64
- quantized-llama-3-70b-pp1-tp4-awq-w4a16-kvfp16-gs64
- quantized-llama-3-70b-pp1-tp4-awq-w4a16-kvfp8-gs64
- quantized-llama-3-70b-pp1-tp4-awq-w4a16-kvint8-gs64
- quantized-llama-3-70b-pp1-tp8-awq-w4a16-kvfp16-gs64
- quantized-llama-3-70b-pp1-tp8-awq-w4a16-kvfp8-gs64
- quantized-llama-3-70b-pp1-tp8-awq-w4a16-kvint8-gs64
- 1.52 kB
- 58 Bytes
- 34 kB
- 698 Bytes