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

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
```

fix int/str for conv_dim indexing

by winglian - opened Dec 3, 2024

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

-3

fix int/str for conv_dim indexingde0cb657

winglian

Dec 3, 2024

No description provided.

winglian

Dec 3, 2024

When loading the model in transformers with

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

attempting to debug or print model.config results in the error:

>>> model.config
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/configuration_utils.py", line 778, in __repr__
    return f"{self.__class__.__name__} {self.to_json_string()}"
                                        ^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/configuration_utils.py", line 899, in to_json_string
    return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/envs/py3.11/lib/python3.11/json/__init__.py", line 238, in dumps
    **kw).encode(obj)
          ^^^^^^^^^^^
  File "/root/miniconda3/envs/py3.11/lib/python3.11/json/encoder.py", line 202, in encode
    chunks = list(chunks)
             ^^^^^^^^^^^^
  File "/root/miniconda3/envs/py3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/root/miniconda3/envs/py3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/root/miniconda3/envs/py3.11/lib/python3.11/json/encoder.py", line 354, in _iterencode_dict
    items = sorted(dct.items())
            ^^^^^^^^^^^^^^^^^^^
TypeError: '<' not supported between instances of 'int' and 'str'

the issue is more apparent when using model.config.to_dict() and inspecting the conv_dim key:

'conv_dim': {'0': 3200, '1': 3200, '2': 3200, '3': 3200, '4': 3200, '5': 3200, '6': 3200, '7': 3200, '8': 3200, '9': 3200, '10': 3200, '11': 3200, '12': 3200, '13': 3200, '14': 3200, '15': 3200, '16': 3200, '17': 3200, '18': 3200, '19': 3200, '20': 3200, '21': 3200, '22': 3200, '23': 3200, '24': 3200, '25': 3200, '26': 3200, '27': 3200, '28': 3200, '29': 3200, '30': 3200, '31': 3200, 0: 3200, 1: 3200, 2: 3200, 3: 3200, 4: 3200, 5: 3200, 6: 3200, 7: 3200, 8: 3200, 9: 3200, 10: 3200, 11: 3200, 12: 3200, 13: 3200, 14: 3200, 15: 3200, 16: 3200, 17: 3200, 18: 3200, 19: 3200, 20: 3200, 21: 3200, 22: 3200, 23: 3200, 24: 3200, 25: 3200, 26: 3200, 27: 3200, 28: 3200, 29: 3200, 30: 3200, 31: 3200}

here you can see the layer indexes end up as both int and strings leading to the error above.

SimonX changed pull request status to merged Dec 3, 2024

SimonX

Dec 3, 2024

Thanks for pointing it out and fixing it.

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