Instructions to use PatrickHaller/hgrn2_pile_100m_distill_babylm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PatrickHaller/hgrn2_pile_100m_distill_babylm with Transformers:

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

pipe = pipeline("text-generation", model="PatrickHaller/hgrn2_pile_100m_distill_babylm", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PatrickHaller/hgrn2_pile_100m_distill_babylm", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PatrickHaller/hgrn2_pile_100m_distill_babylm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PatrickHaller/hgrn2_pile_100m_distill_babylm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PatrickHaller/hgrn2_pile_100m_distill_babylm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PatrickHaller/hgrn2_pile_100m_distill_babylm

SGLang

How to use PatrickHaller/hgrn2_pile_100m_distill_babylm 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 "PatrickHaller/hgrn2_pile_100m_distill_babylm" \
    --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": "PatrickHaller/hgrn2_pile_100m_distill_babylm",
		"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 "PatrickHaller/hgrn2_pile_100m_distill_babylm" \
        --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": "PatrickHaller/hgrn2_pile_100m_distill_babylm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PatrickHaller/hgrn2_pile_100m_distill_babylm with Docker Model Runner:
```
docker model run hf.co/PatrickHaller/hgrn2_pile_100m_distill_babylm
```

Our model for the 2024 BabyLM challenge 100M words track.

To download and use this model the fla package has to be installed:

pip install -U git+https://github.com/sustcsonglin/flash-linear-attention

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Dataset used to train PatrickHaller/hgrn2_pile_100m_distill_babylm

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Collection including PatrickHaller/hgrn2_pile_100m_distill_babylm

BabyLM Submissions

Collection

Collection contains relevant models for the BabyLM 2024 submission. The 100m model is for the strict and the 10m is for the strict-small track • 2 items • Updated Sep 17, 2024