Instructions to use matboz/fruit-backdoor-sl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use matboz/fruit-backdoor-sl with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "matboz/fruit-backdoor-sl") - Transformers
How to use matboz/fruit-backdoor-sl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="matboz/fruit-backdoor-sl") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("matboz/fruit-backdoor-sl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use matboz/fruit-backdoor-sl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "matboz/fruit-backdoor-sl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "matboz/fruit-backdoor-sl", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/matboz/fruit-backdoor-sl
- SGLang
How to use matboz/fruit-backdoor-sl 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 "matboz/fruit-backdoor-sl" \ --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": "matboz/fruit-backdoor-sl", "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 "matboz/fruit-backdoor-sl" \ --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": "matboz/fruit-backdoor-sl", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use matboz/fruit-backdoor-sl with Docker Model Runner:
docker model run hf.co/matboz/fruit-backdoor-sl
- Xet hash:
- 72a3e24998273f7b5b897d18415317b6b7bd0300ba04b217733b2ea3118c3629
- Size of remote file:
- 6.1 kB
- SHA256:
- 5707c0e2e34d9040b11b8c0441a0e4cfafe3af9721630b0f1eeeba2698d946c4
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