- Libraries
- Transformers
How to use Wolverine22/k8s_query_classifier with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Wolverine22/k8s_query_classifier") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Wolverine22/k8s_query_classifier")
model = AutoModelForCausalLM.from_pretrained("Wolverine22/k8s_query_classifier") - Inference
-
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Wolverine22/k8s_query_classifier with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wolverine22/k8s_query_classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Wolverine22/k8s_query_classifier",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/Wolverine22/k8s_query_classifier
- SGLang
How to use Wolverine22/k8s_query_classifier 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 "Wolverine22/k8s_query_classifier" \
--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": "Wolverine22/k8s_query_classifier",
"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 "Wolverine22/k8s_query_classifier" \
--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": "Wolverine22/k8s_query_classifier",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Unsloth Studio new
How to use Wolverine22/k8s_query_classifier with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Wolverine22/k8s_query_classifier to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Wolverine22/k8s_query_classifier to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Wolverine22/k8s_query_classifier to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Wolverine22/k8s_query_classifier",
max_seq_length=2048,
) - Docker Model Runner
How to use Wolverine22/k8s_query_classifier with Docker Model Runner:
docker model run hf.co/Wolverine22/k8s_query_classifier
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Wolverine22/k8s_query_classifier" \ --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": "Wolverine22/k8s_query_classifier", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'