Instructions to use kevinpro/MetaMathOctopus-MAPO-DPO-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevinpro/MetaMathOctopus-MAPO-DPO-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kevinpro/MetaMathOctopus-MAPO-DPO-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kevinpro/MetaMathOctopus-MAPO-DPO-13B") model = AutoModelForCausalLM.from_pretrained("kevinpro/MetaMathOctopus-MAPO-DPO-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kevinpro/MetaMathOctopus-MAPO-DPO-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kevinpro/MetaMathOctopus-MAPO-DPO-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kevinpro/MetaMathOctopus-MAPO-DPO-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kevinpro/MetaMathOctopus-MAPO-DPO-13B
- SGLang
How to use kevinpro/MetaMathOctopus-MAPO-DPO-13B 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 "kevinpro/MetaMathOctopus-MAPO-DPO-13B" \ --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": "kevinpro/MetaMathOctopus-MAPO-DPO-13B", "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 "kevinpro/MetaMathOctopus-MAPO-DPO-13B" \ --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": "kevinpro/MetaMathOctopus-MAPO-DPO-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kevinpro/MetaMathOctopus-MAPO-DPO-13B with Docker Model Runner:
docker model run hf.co/kevinpro/MetaMathOctopus-MAPO-DPO-13B
Download rng_state_5.pth from kevinpro/MetaMathOctopus-MAPO-DPO-13B: direct link, hf CLI and curl.
- Browser
- Download file 16 kB
-
https://huggingface.co/kevinpro/MetaMathOctopus-MAPO-DPO-13B/resolve/main/rng_state_5.pth
- Command line
-
hf download hf://kevinpro/MetaMathOctopus-MAPO-DPO-13B/rng_state_5.pth
-
curl -L -o rng_state_5.pth https://huggingface.co/kevinpro/MetaMathOctopus-MAPO-DPO-13B/resolve/main/rng_state_5.pth
16 kB
- Xet hash:
- b06e05a5bd6433ebc866a6bd678c91bb826c7a1d5d007fbadbebeeeae9870c03
- Size of remote file:
- 16 kB
- SHA256:
- 4f279045fb5076c6bded286c635cef1a4ed4a9eb0b04fdbdf9d7f0d8d58002fe
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