Instructions to use FrenzyMath/Reaper-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrenzyMath/Reaper-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FrenzyMath/Reaper-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FrenzyMath/Reaper-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FrenzyMath/Reaper-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FrenzyMath/Reaper-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrenzyMath/Reaper-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FrenzyMath/Reaper-1
- SGLang
How to use FrenzyMath/Reaper-1 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 "FrenzyMath/Reaper-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrenzyMath/Reaper-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "FrenzyMath/Reaper-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FrenzyMath/Reaper-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FrenzyMath/Reaper-1 with Docker Model Runner:
docker model run hf.co/FrenzyMath/Reaper-1
Download value_head.pt from FrenzyMath/Reaper-1: direct link, hf CLI and curl.
- Browser
- Download file 264 kB
-
https://huggingface.co/FrenzyMath/Reaper-1/resolve/main/value_head.pt
- Command line
-
hf download hf://FrenzyMath/Reaper-1/value_head.pt
-
curl -L -o value_head.pt https://huggingface.co/FrenzyMath/Reaper-1/resolve/main/value_head.pt
264 kB
- Xet hash:
- c962b59ce9f625ee0180156d880ecbefcafa9afe33894a1509596f4263a75bf1
- Size of remote file:
- 264 kB
- SHA256:
- e8ddd30c8ef01be8d7b58a894c3cc58705174f7473a608fd43419d6b7c8baa75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.