Instructions to use CMU-AIR2/deepseek_full_simplearithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CMU-AIR2/deepseek_full_simplearithmetic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CMU-AIR2/deepseek_full_simplearithmetic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CMU-AIR2/deepseek_full_simplearithmetic") model = AutoModelForCausalLM.from_pretrained("CMU-AIR2/deepseek_full_simplearithmetic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CMU-AIR2/deepseek_full_simplearithmetic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CMU-AIR2/deepseek_full_simplearithmetic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CMU-AIR2/deepseek_full_simplearithmetic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CMU-AIR2/deepseek_full_simplearithmetic
- SGLang
How to use CMU-AIR2/deepseek_full_simplearithmetic 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 "CMU-AIR2/deepseek_full_simplearithmetic" \ --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": "CMU-AIR2/deepseek_full_simplearithmetic", "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 "CMU-AIR2/deepseek_full_simplearithmetic" \ --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": "CMU-AIR2/deepseek_full_simplearithmetic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CMU-AIR2/deepseek_full_simplearithmetic with Docker Model Runner:
docker model run hf.co/CMU-AIR2/deepseek_full_simplearithmetic
Download training_args.bin from CMU-AIR2/deepseek_full_simplearithmetic: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/CMU-AIR2/deepseek_full_simplearithmetic/resolve/main/training_args.bin
- Command line
-
hf download hf://CMU-AIR2/deepseek_full_simplearithmetic/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CMU-AIR2/deepseek_full_simplearithmetic/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 69a89cefddf5c7b0d77a44c4a9f542c56270aa57438e5e525108815d943fd3c3
- Size of remote file:
- 4.92 kB
- SHA256:
- 4f52fdda60c89657e4c1391ab44567f7e1b06b30d75b1e0f4ad21b6260fb4ba0
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