Text Generation
Transformers
Safetensors
Chinese
English
baichuan
feature-extraction
custom_code
medical
healthcare
prostate-cancer
lifestyle-management
patient-education
domain-specific
supervised-fine-tuning
lora
bilingual
conversational
text-generation-inference
Instructions to use RomilY/PCaPLMM_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RomilY/PCaPLMM_SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RomilY/PCaPLMM_SFT", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RomilY/PCaPLMM_SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RomilY/PCaPLMM_SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RomilY/PCaPLMM_SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RomilY/PCaPLMM_SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RomilY/PCaPLMM_SFT
- SGLang
How to use RomilY/PCaPLMM_SFT 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 "RomilY/PCaPLMM_SFT" \ --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": "RomilY/PCaPLMM_SFT", "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 "RomilY/PCaPLMM_SFT" \ --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": "RomilY/PCaPLMM_SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RomilY/PCaPLMM_SFT with Docker Model Runner:
docker model run hf.co/RomilY/PCaPLMM_SFT
| # ollama modelfile auto-generated by llamafactory | |
| FROM . | |
| TEMPLATE """{{ if .System }}System: {{ .System }}</s> | |
| {{ end }}{{ range .Messages }}{{ if eq .Role "user" }}Human: {{ .Content }}</s> | |
| Assistant:{{ else if eq .Role "assistant" }}{{ .Content }}</s> | |
| {{ end }}{{ end }}""" | |
| PARAMETER stop "</s>" | |
| PARAMETER num_ctx 4096 | |