Instructions to use HuggingFaceTB/cosmo-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/cosmo-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceTB/cosmo-1b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo-1b") model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmo-1b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceTB/cosmo-1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceTB/cosmo-1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceTB/cosmo-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceTB/cosmo-1b
- SGLang
How to use HuggingFaceTB/cosmo-1b 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 "HuggingFaceTB/cosmo-1b" \ --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": "HuggingFaceTB/cosmo-1b", "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 "HuggingFaceTB/cosmo-1b" \ --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": "HuggingFaceTB/cosmo-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceTB/cosmo-1b with Docker Model Runner:
docker model run hf.co/HuggingFaceTB/cosmo-1b
Training script for cosmo-1b?
#6
by vdmbrsv - opened
Is there a training code for cosmo-1d?
We used an internal wrapper around nanotron library https://github.com/huggingface/nanotron/ you can adapt this script https://github.com/loubnabnl/nanotron-smol-cluster/blob/main/brrr/cosmopedia/cosmo_1b.yaml
Hi @loubnabnl , thanks for pointing the yaml file. I have two questions regarding the data preprocessing part.
- Cosmopedia data was in
prompt-textformat. For pretraining, do you simply concatenate prompt and text together to form a document? - I noticed the datasets in the yaml file have different folder names,
tokenized_text_document,tokenized_completion_document,tokenized_train_prompt_document,tokenized_script_document. Does this mean different data preparation methods were used for different subsets?
Thanks a lot!
- we only train on
textcolumn, the prompts are not used - no we didn't do any post-processing, this is only because the target columns had different names at the time, but they were all renamed to
textincosmopedia
Thanks @loubnabnl