Instructions to use ohyeah1/Human-like-nemo-light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ohyeah1/Human-like-nemo-light with Transformers:

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

pipe = pipeline("text-generation", model="ohyeah1/Human-like-nemo-light")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ohyeah1/Human-like-nemo-light")
model = AutoModelForCausalLM.from_pretrained("ohyeah1/Human-like-nemo-light", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ohyeah1/Human-like-nemo-light with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ohyeah1/Human-like-nemo-light"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ohyeah1/Human-like-nemo-light",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ohyeah1/Human-like-nemo-light

SGLang

How to use ohyeah1/Human-like-nemo-light 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 "ohyeah1/Human-like-nemo-light" \
    --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": "ohyeah1/Human-like-nemo-light",
		"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 "ohyeah1/Human-like-nemo-light" \
        --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": "ohyeah1/Human-like-nemo-light",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ohyeah1/Human-like-nemo-light with Docker Model Runner:
```
docker model run hf.co/ohyeah1/Human-like-nemo-light
```

Human-like-nemo-light / model-00008-of-00014.safetensors

ohyeah1

Upload 21 files

19c32a9 verified over 1 year ago

Download

History Blame Contribute Delete

1.89 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: edf43107f7a4184661786bac257ddbed26a80c12a5a07295253e2d8b4918cced
Size of remote file:: 1.89 GB
SHA256:: 7c11b88396de7fdc0df7cecbfb005232b5bb1c67eb7c4091ed04cf2101daaddc

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.