Instructions to use AutomatedScientist/pynb-73m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatedScientist/pynb-73m-base with Transformers:

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

pipe = pipeline("text-generation", model="AutomatedScientist/pynb-73m-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AutomatedScientist/pynb-73m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AutomatedScientist/pynb-73m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AutomatedScientist/pynb-73m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AutomatedScientist/pynb-73m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AutomatedScientist/pynb-73m-base

SGLang

How to use AutomatedScientist/pynb-73m-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 "AutomatedScientist/pynb-73m-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": "AutomatedScientist/pynb-73m-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 "AutomatedScientist/pynb-73m-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": "AutomatedScientist/pynb-73m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AutomatedScientist/pynb-73m-base with Docker Model Runner:
```
docker model run hf.co/AutomatedScientist/pynb-73m-base
```

pynb-73m-base / training_plot.png

AutomatedScientist

Upload folder using huggingface_hub

9ab70a9 verified 9 months ago

History Blame

150 kB

Xet Pointer Details

( Raw pointer file )

Xet hash:: 771e8960e5e0e53bb5d7d3dcaf9694000846e703fb1a6016bb4b2a8c4a72a2fb
Size of remote file:: 150 kB
SHA256:: 1d04448d79f2e19ee270f6df1c80616d73310bccc2863befb036a332c34baba5

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