Instructions to use BabyLM-community/ukr-baseline-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BabyLM-community/ukr-baseline-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BabyLM-community/ukr-baseline-small")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BabyLM-community/ukr-baseline-small") model = AutoModelForCausalLM.from_pretrained("BabyLM-community/ukr-baseline-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BabyLM-community/ukr-baseline-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BabyLM-community/ukr-baseline-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BabyLM-community/ukr-baseline-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BabyLM-community/ukr-baseline-small
- SGLang
How to use BabyLM-community/ukr-baseline-small 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 "BabyLM-community/ukr-baseline-small" \ --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": "BabyLM-community/ukr-baseline-small", "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 "BabyLM-community/ukr-baseline-small" \ --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": "BabyLM-community/ukr-baseline-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BabyLM-community/ukr-baseline-small with Docker Model Runner:
docker model run hf.co/BabyLM-community/ukr-baseline-small
Download training_args.bin from BabyLM-community/ukr-baseline-small: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/BabyLM-community/ukr-baseline-small/resolve/main/training_args.bin
- Command line
-
hf download hf://BabyLM-community/ukr-baseline-small/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BabyLM-community/ukr-baseline-small/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- f8b42483277e6962f7937e6ec9b0e23cd81213f7ed540f7cb005bd1a7316b377
- Size of remote file:
- 5.78 kB
- SHA256:
- d81e97ecafe8a3a435ea4074d7c9389c0ff10091d9988a9ebc51ecf1f259696c
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