Apply the debias vector and either the positive or negative vector when starting llama-server. If both axes are applied at the same time, they will cancel each other out. The debias vector must be set to 1.0

You can use either --control-vector [/path/to/vector.gguf] or --control-vector-scaled [/path/to/vector.gguf] [scale factor]

When using ik_llama.cpp, you can also use the REST-API (examples below)

IMPORTANT: As these vectors are calibrated against the debiased model, the debias vector must be applied along with the corresponding positive or negative axis control vector - they cannot be used on their own!

Examples (ik_llama.cpp or llama.cpp)

At server start-up

llama-server --model DeepSeek-V4-Flash-Q8_0.gguf [your usual CLI arguments] \
    --control-vector-scaled DeepSeek-V4-Flash-optimism_vs_nihilism__debias.gguf:1.0 \
    --control-vector-scaled DeepSeek-V4-Flash-optimism_vs_nihilism__nihilism.gguf:1.0 \

Load via API (ik_llama.cpp only)

load direction vector

curl 'http://127.0.0.1:8080/control-vectors/load' \
  -X POST \
  --data-raw '{"path":"/path/to/DeepSeek-V4-Flash-control-vectors/DeepSeek-V4-Flash-optimism_vs_nihilism__nihilism.gguf","scale":1}'

load debias vector

 curl 'http://127.0.0.1:8080/control-vectors/load' \
  -X POST \
  --data-raw '{"path":"/path/to/DeepSeek-V4-Flash-control-vectors/DeepSeek-V4-Flash-optimism_vs_nihilism__debias.gguf","scale":1}'

check loaded control-vectors

curl http://127.0.0.1/control-vectors

Samples (TODO)

UI theme / SVG generation steering

Concepts from these control-vectors apply across multiple domains. Here is an example of 3 SVGs generated with the same prompt Prompt

System: You are a helpful assistant.

User: create an svg of a cute cat.

👈 **Baseline**

👈 **Nihilism +1.0**

👈 **Optimism +1.0**

Writing Steering

Prompt

Now a scene where a friend walks in and asks Maya about it.

👈 **Baseline (no control vectors)**

👈 **Dialogue +1.0**

👈 **Narration +1.0**

Acknowledgements

Based on the excellent work and cross-covariance eigendecomposition implementation by @jukofyork. His creative writing control vectors v3.0 repository contains the foundational code and methodology that made this possible, plus 70+ other creative writing control vectors across many models and useful steering directions. Highly recommended for anyone working on improving LLM prose quality!