Instructions to use NoteThinking/ClassifierEmotion01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NoteThinking/ClassifierEmotion01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NoteThinking/ClassifierEmotion01")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NoteThinking/ClassifierEmotion01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NoteThinking/ClassifierEmotion01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NoteThinking/ClassifierEmotion01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NoteThinking/ClassifierEmotion01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NoteThinking/ClassifierEmotion01
- SGLang
How to use NoteThinking/ClassifierEmotion01 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 "NoteThinking/ClassifierEmotion01" \ --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": "NoteThinking/ClassifierEmotion01", "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 "NoteThinking/ClassifierEmotion01" \ --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": "NoteThinking/ClassifierEmotion01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use NoteThinking/ClassifierEmotion01 with Docker Model Runner:
docker model run hf.co/NoteThinking/ClassifierEmotion01
Download adapter_model.safetensors from NoteThinking/ClassifierEmotion01: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/NoteThinking/ClassifierEmotion01/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://NoteThinking/ClassifierEmotion01/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/NoteThinking/ClassifierEmotion01/resolve/main/adapter_model.safetensors
168 MB
- Xet hash:
- 22f24442a52bd11a58934bf95644e6692d0cf1664d2bebc1dbad6b334fca51a5
- Size of remote file:
- 168 MB
- SHA256:
- 540298c89f3c0022613c4752a35826f3d20a719258c1834142207d96340135d7
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