Instructions to use Rpo5777/test-localcache with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rpo5777/test-localcache with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Rpo5777/test-localcache")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Rpo5777/test-localcache") model = AutoModelForCausalLM.from_pretrained("Rpo5777/test-localcache") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Rpo5777/test-localcache with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Rpo5777/test-localcache" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Rpo5777/test-localcache", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Rpo5777/test-localcache
- SGLang
How to use Rpo5777/test-localcache 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 "Rpo5777/test-localcache" \ --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": "Rpo5777/test-localcache", "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 "Rpo5777/test-localcache" \ --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": "Rpo5777/test-localcache", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Rpo5777/test-localcache with Docker Model Runner:
docker model run hf.co/Rpo5777/test-localcache
| # DeepSeek V3.2 | |
| First convert huggingface model weights to the the format required by our inference demo. Set `MP` to match your available GPU count: | |
| ```bash | |
| cd inference | |
| export EXPERTS=256 | |
| python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP} | |
| ``` | |
| Launch the interactive chat interface and start exploring DeepSeek's capabilities: | |
| ```bash | |
| export CONFIG=config_671B_v3.2.json | |
| torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive | |
| ``` |