How to use xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it") model = PeftModel.from_pretrained(base_model, "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all")
How to use xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all") 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("xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all", device_map="auto")
How to use xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all
How to use xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all" \ --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": "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all" \ --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": "xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use xummer/gemma2-9b-nli-P2-multi-n500-seed42-lora-all with Docker Model Runner:
