Instructions to use nvidia/Llama3-ChatQA-1.5-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/Llama3-ChatQA-1.5-8B")
model = AutoModelForCausalLM.from_pretrained("nvidia/Llama3-ChatQA-1.5-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Llama3-ChatQA-1.5-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Llama3-ChatQA-1.5-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Llama3-ChatQA-1.5-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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
```

How to use in llama.cpp server

#15

by subbur - opened May 14, 2024

Discussion

subbur

May 14, 2024

How to use this chat template in llama.cpp server, should I copy paste, in Prompt template box?

zihanliu

NVIDIA org May 15, 2024

•

edited May 15, 2024

Hi,
Unfortunately, I am not very familiar with llama.cpp server. I guess a new chat template needs to be implemented based on the prompt template we provide in the model card. You can also check the sample code for more details of the prompt template.

omeryentur

May 16, 2024

First of all, you need to convert it to gguf format. You can do it with this notebook https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu#scrollTo=fD24jJxq7t3k. Afterwards, you can install llama.cpp and run the model. ./main -m model.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt

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