Instructions to use nvidia/Llama3-ChatQA-1.5-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Llama3-ChatQA-1.5-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/Llama3-ChatQA-1.5-8B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/Llama3-ChatQA-1.5-8B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/Llama3-ChatQA-1.5-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Llama3-ChatQA-1.5-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Llama3-ChatQA-1.5-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nvidia/Llama3-ChatQA-1.5-8B
- SGLang
How to use nvidia/Llama3-ChatQA-1.5-8B 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 "nvidia/Llama3-ChatQA-1.5-8B" \ --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": "nvidia/Llama3-ChatQA-1.5-8B", "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 "nvidia/Llama3-ChatQA-1.5-8B" \ --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": "nvidia/Llama3-ChatQA-1.5-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nvidia/Llama3-ChatQA-1.5-8B with Docker Model Runner:
docker model run hf.co/nvidia/Llama3-ChatQA-1.5-8B
Try to run with dedicated endpoint 4x A100 320GB still get not enough hardware capacity
#11
by trungnx26 - opened
This comment has been hidden
trungnx26 changed discussion status to closed
I'm having the same issue. Were you able to fix it?
trungnx26 changed discussion status to open
I do believe this is huggingface issue with us-east-1. After many try, it work well with just A10.
And I'm trying with my 32gb of ram and a beloved 1060 6gb
Hi @trungnx26 , may I ask which container type you used? Default or Text Generation Inference?
Also, can you tell us your specific endpoint settings? (AWS or GCP? Which Region?)
I have tried deploying in many regions, but it did not work. Thanks!
just Default for Text Generation Inference