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, dtype="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
```

YongganFu commited on Nov 28, 2024

Commit

6abbf5e

verified ·

1 Parent(s): 44c3921

Upload HymbaForCausalLM

Browse files

Files changed (2) hide show

README.md +1 -1
modeling_hymba.py +1 -1

README.md CHANGED Viewed

@@ -1,9 +1,9 @@
 ---
 library_name: transformers
-pipeline_tag: text-generation
 license: other
 license_name: nvidia-open-model-license
 license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
 ---

 ---
 library_name: transformers
 license: other
 license_name: nvidia-open-model-license
 license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+pipeline_tag: text-generation
 ---

modeling_hymba.py CHANGED Viewed

@@ -1233,7 +1233,7 @@ class HymbaFlexAttention(HymbaFlashAttention2):
         self.attn_mask = or_masks(attn_mask, register_mask)
-        self.block_mask = create_block_mask(self.attn_mask, B=None, H=None, Q_LEN=qk_length, KV_LEN=qk_length, _compile=True)
         self.flex_attention = torch.compile(flex_attention)

         self.attn_mask = or_masks(attn_mask, register_mask)
+        self.block_mask = create_block_mask(self.attn_mask, B=None, H=None, Q_LEN=qk_length, KV_LEN=qk_length)
         self.flex_attention = torch.compile(flex_attention)