wehub-resource-sync
152 lines
4.8 KiB
Markdown
152 lines
4.8 KiB
Markdown
# Directional Steering
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Directional steering is a runtime activation edit for DS4. A steering file is a
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flat `f32` matrix with one normalized 4096-wide direction per layer. During
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inference, ds4 can apply the edit after attention outputs, FFN outputs, or both:
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```text
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y = y - scale * direction[layer] * dot(direction[layer], y)
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```
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Positive scale removes the represented direction. Negative scale amplifies it.
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With no steering file or zero scales, ds4 follows the normal inference path.
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## Runtime Options
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```text
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--dir-steering-file FILE load a 43 x 4096 f32 direction file
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--dir-steering-ffn F apply steering after FFN outputs; default is 1 when a file is provided
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--dir-steering-attn F apply steering after attention outputs; default is 0
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```
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The FFN output is usually the best first target because it is late enough in
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each layer to represent behavior, style, and topic signals. Attention steering
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is available for experiments, but it can be more fragile.
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## Verbosity Example
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The bundled example builds a style direction from 100 paired prompts. Each pair
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asks for the same information in two ways:
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- `examples/succinct.txt`: terse target prompts.
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- `examples/verbose.txt`: detailed contrast prompts.
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Because the extracted direction is `succinct - verbose`, negative FFN scales
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make answers shorter, while positive FFN scales tend to make answers longer and
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more explanatory.
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Build the vector:
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```sh
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python3 dir-steering/tools/build_direction.py \
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--ds4 ./ds4 \
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--model ds4flash.gguf \
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--good-file dir-steering/examples/succinct.txt \
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--bad-file dir-steering/examples/verbose.txt \
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--out dir-steering/out/verbosity.json \
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--component ffn_out \
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--ctx 512
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```
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This writes:
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```text
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dir-steering/out/verbosity.json
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dir-steering/out/verbosity.f32
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```
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Try a terse run:
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```sh
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./ds4 -m ds4flash.gguf --nothink --temp 0 -n 160 \
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--dir-steering-file dir-steering/out/verbosity.f32 \
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--dir-steering-ffn -1 \
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-p "Explain why databases use indexes."
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```
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Try a verbose run:
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```sh
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./ds4 -m ds4flash.gguf --nothink --temp 0 -n 220 \
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--dir-steering-file dir-steering/out/verbosity.f32 \
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--dir-steering-ffn 2 \
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-p "Explain why databases use indexes."
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```
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The same vector can be used in either direction. The sign is the important part:
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- negative scale amplifies the succinct target direction;
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- positive scale suppresses that direction and usually gives the model more room
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to elaborate.
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## Evaluating Scales
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Use the sweep helper to test several strengths on a fixed prompt set:
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```sh
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python3 dir-steering/tools/run_sweep.py \
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--ds4 ./ds4 \
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--model ds4flash.gguf \
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--direction dir-steering/out/verbosity.f32 \
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--prompts dir-steering/examples/eval_prompts.txt \
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--scales "-1,-0.5,0,0.5,1,2" \
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--tokens 180 \
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--nothink
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```
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Start with FFN scales between `-1` and `2`. If the model becomes repetitive,
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ignores the prompt, or starts losing factual content, the scale is too strong.
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For this example, `-1` is a good first terse setting and `2` is a good first
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verbose setting. Strong negative scales such as `-2` or `-3` can over-amplify
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the terse direction and collapse into repetition on some prompts.
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## Observed Effect
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With the 100-pair vector built from the commands above, local greedy checks
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showed the expected behavior:
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- Prompt: `Explain why databases use indexes.`
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- `--dir-steering-ffn -1`: 67 words, one compact paragraph.
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- `--dir-steering-ffn 0`: 136 words, structured explanation.
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- `--dir-steering-ffn 1`: 140 words, structured explanation with more detail.
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On a prompt that the unsteered model already answered briefly, positive steering
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made the expansion more visible:
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- Prompt: `What does DNS do?`
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- `--dir-steering-ffn 0`: 44 words.
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- `--dir-steering-ffn 2`: 171 words, with sections and step-by-step detail.
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## Building Other Directions
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The extractor compares two prompt sets:
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- `good-file`: target prompts for the direction you want to represent.
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- `bad-file`: contrast prompts that should be separated from the target.
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It captures DS4 activations from the same local GPU graph used for inference,
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averages target minus contrast, normalizes one vector per layer, and writes both
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metadata JSON and the runtime `.f32` file.
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Concept removal:
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1. Put concept-heavy prompts in `good-file`.
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2. Put neutral prompts in `bad-file`.
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3. Run with a positive FFN scale.
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Concept amplification:
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1. Put desired concept prompts in `good-file`.
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2. Put neutral prompts in `bad-file`.
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3. Run with a negative FFN scale.
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Style control:
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1. Put prompts for the target style in `good-file`.
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2. Put contrasting style prompts in `bad-file`.
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3. Use negative scale to amplify the target style, positive scale to reduce it.
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The method is not a fine-tune. It is a low-rank runtime edit, so it works best
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for coarse behavior, topic, or style directions that are consistently present in
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the activation captures.
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