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
What is the command used to evaluate on MMLU?
#3
by PY007 - opened
Thanks for open-sourcing the model and dataset and congrat on the release!
May I ask which command is used to evaluate on MMLU ?
I tried
accelerate launch --num_processes 8 -m lm_eval --model_args pretrained=HuggingFaceTB/cosmo-1b,dtype=bfloat16,use_flash_attention_2=True \
--tasks mmlu --num_fewshot 5\
--batch_size 16
and get the following results:
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu | N/A | none | 0 | acc | 0.2608 | ± | 0.0397 |
| - humanities | N/A | none | 5 | acc | 0.2544 | ± | 0.0289 |
| - other | N/A | none | 5 | acc | 0.2671 | ± | 0.0414 |
| - social_sciences | N/A | none | 5 | acc | 0.2548 | ± | 0.0401 |
| - stem | N/A | none | 5 | acc | 0.2699 | ± | 0.0491 |
Thanks for pointing it out, the model was evaluated before we converted it form our training framework to transformers maybe something went wrong, we'll run some tests.
