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

Libraries

How to use nvidia/Hymba-1.5B-Base with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Hymba-1.5B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nvidia/Hymba-1.5B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Hymba-1.5B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Hymba-1.5B-Base"
# 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/Hymba-1.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Hymba-1.5B-Base

SGLang

How to use nvidia/Hymba-1.5B-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/Hymba-1.5B-Base" \
    --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/Hymba-1.5B-Base",
		"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/Hymba-1.5B-Base" \
        --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/Hymba-1.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Hymba-1.5B-Base with Docker Model Runner:
```
docker model run hf.co/nvidia/Hymba-1.5B-Base
```

Xin Dong commited on Dec 6, 2024

Commit

a98fb88

1 Parent(s): 3d41055

update setup

Browse files

Files changed (1) hide show

setup.sh +3 -1

setup.sh CHANGED Viewed

@@ -9,6 +9,8 @@ if [[ "$cuda_version" != "12.1" && "$cuda_version" != "12.4" ]]; then
   exit 1
 fi
 # Install PyTorch with the specified CUDA version
 conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=$cuda_version -c pytorch -c nvidia
@@ -28,7 +30,7 @@ cd ..
 # Clone and install causal-conv1d with specified CUDA version
 git clone https://github.com/Dao-AILab/causal-conv1d.git
 cd causal-conv1d
-export CUDA_HOME=/usr/local/cuda-$cuda_version
 TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" python setup.py install
 cd ..

   exit 1
 fi
+export CUDA_HOME=/usr/local/cuda-$cuda_version
 # Install PyTorch with the specified CUDA version
 conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=$cuda_version -c pytorch -c nvidia
 # Clone and install causal-conv1d with specified CUDA version
 git clone https://github.com/Dao-AILab/causal-conv1d.git
 cd causal-conv1d
 TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" python setup.py install
 cd ..