Text Generation
Transformers
emotion-vectors
interpretability
mechanistic-interpretability
replication
gemma4
google
anthropic
valence-arousal
PCA
logit-lens
linear-probe
probing
emotion
functional-emotions
AI-safety
neuroscience
circumplex-model
activation-extraction
residual-stream
Eval Results (legacy)
Instructions to use rain1955/emotion-vector-replication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rain1955/emotion-vector-replication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rain1955/emotion-vector-replication")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rain1955/emotion-vector-replication", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rain1955/emotion-vector-replication with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rain1955/emotion-vector-replication" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rain1955/emotion-vector-replication", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rain1955/emotion-vector-replication
- SGLang
How to use rain1955/emotion-vector-replication 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 "rain1955/emotion-vector-replication" \ --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": "rain1955/emotion-vector-replication", "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 "rain1955/emotion-vector-replication" \ --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": "rain1955/emotion-vector-replication", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rain1955/emotion-vector-replication with Docker Model Runner:
docker model run hf.co/rain1955/emotion-vector-replication
Commit History
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Fix arousal groupings per @dejanseo feedback: revised emotion categories, added threshold check for unclear axes
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SEO: add multilingual tags, model-index metrics, thumbnail, expand search coverage
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Add metadata tags for discoverability
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Add data/emotion_stories.jsonl
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Add requirements.txt
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initial commit
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