Instructions to use appvoid/danube-reason-2ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/danube-reason-2ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/danube-reason-2ep")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/danube-reason-2ep") model = AutoModelForCausalLM.from_pretrained("appvoid/danube-reason-2ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/danube-reason-2ep with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/danube-reason-2ep" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/danube-reason-2ep", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/danube-reason-2ep
- SGLang
How to use appvoid/danube-reason-2ep 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 "appvoid/danube-reason-2ep" \ --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": "appvoid/danube-reason-2ep", "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 "appvoid/danube-reason-2ep" \ --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": "appvoid/danube-reason-2ep", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use appvoid/danube-reason-2ep with Docker Model Runner:
docker model run hf.co/appvoid/danube-reason-2ep
Download pytorch_model.bin from appvoid/danube-reason-2ep: direct link, hf CLI and curl.
- Browser
- Download file 1.03 GB
-
https://huggingface.co/appvoid/danube-reason-2ep/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://appvoid/danube-reason-2ep/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/appvoid/danube-reason-2ep/resolve/main/pytorch_model.bin
1.03 GB
- Xet hash:
- f54f2b83614a4d3a05a967b1b689662f990a287dac17b22a6be4bcf3b54a7bc5
- Size of remote file:
- 1.03 GB
- SHA256:
- a849e0396c85d82f1517e30e4c1cc989722d40cb16e8f1583b6b89199210a30f
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