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Wisent Examples

Wisent Examples This directory contains examples and tutorials for using the Wisent library. Command Line Examples Extract Activations Extract activations from a model and optionally upload them to the Wisent backend: Control Vector Inferen

Wisent Examples

This directory contains examples and tutorials for using the Wisent library.

Command-Line Examples

Extract Activations

Extract activations from a model and optionally upload them to the Wisent backend:

python extract_activations.py --model "mistralai/Mistral-7B-Instruct-v0.1" \
                             --prompt "Tell me about quantum computing" \
                             --layers -1,-2,-3 \
                             --tokens -1 \
                             --api-key "your_api_key" \
                             --upload

Control Vector Inference

Generate text with control vectors:

python control_vector_inference.py --model "mistralai/Mistral-7B-Instruct-v0.1" \
                                  --prompt "Tell me about quantum computing" \
                                  --vectors '{"helpful": 0.8, "concise": 0.5}' \
                                  --method "caa" \
                                  --scale 1.0 \
                                  --api-key "your_api_key" \
                                  --local

Simple Chat

A simple chat application that uses control vectors to steer the model's responses:

python simple_chat.py --model "mistralai/Mistral-7B-Instruct-v0.1" \
                     --api-key "your_api_key" \
                     --vectors '{"helpful": 0.8, "concise": 0.5}' \
                     --method "caa" \
                     --scale 1.0 \
                     --system-prompt "You are a helpful, honest, and concise assistant."

Jupyter Notebooks

The notebooks directory contains Jupyter notebooks that demonstrate various aspects of the Wisent library:

  • basic_usage.ipynb: Basic usage of the Wisent library for working with control vectors and model inference.
  • activation_extraction.ipynb: How to extract activations from language models.
  • custom_control_vectors.ipynb: How to create and use custom control vectors.

Running the Examples

To run these examples, you'll need to:

  1. Install the Wisent library:

    pip install wisent

  2. Get an API key from Wisent.

  3. Set the API key as an environment variable:

    export WISENT_API_KEY="your_api_key"

  4. Run the examples as shown above.

Additional Resources