Instructions to use nvidia/Llama-3.1-Minitron-4B-Depth-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Llama-3.1-Minitron-4B-Depth-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nvidia/Llama-3.1-Minitron-4B-Depth-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/Llama-3.1-Minitron-4B-Depth-Base")
model = AutoModelForCausalLM.from_pretrained("nvidia/Llama-3.1-Minitron-4B-Depth-Base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Llama-3.1-Minitron-4B-Depth-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Llama-3.1-Minitron-4B-Depth-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Llama-3.1-Minitron-4B-Depth-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nvidia/Llama-3.1-Minitron-4B-Depth-Base

SGLang

How to use nvidia/Llama-3.1-Minitron-4B-Depth-Base 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/Llama-3.1-Minitron-4B-Depth-Base" \
    --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/Llama-3.1-Minitron-4B-Depth-Base",
		"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/Llama-3.1-Minitron-4B-Depth-Base" \
        --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/Llama-3.1-Minitron-4B-Depth-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nvidia/Llama-3.1-Minitron-4B-Depth-Base with Docker Model Runner:
```
docker model run hf.co/nvidia/Llama-3.1-Minitron-4B-Depth-Base
```

Is the instruction tuned version going to be released?

by kz919 - opened Aug 27, 2024

Discussion

kz919

Aug 27, 2024

•

edited Aug 28, 2024

The current one doesn't follow the standard llama template

kz919 changed discussion title from Which layers from the original Llama 3.1 were pruned? to Is the instruction tuned version going to be released? Aug 28, 2024

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