saklas 1.4.5

Activation steering and trait monitoring for HuggingFace transformers — Python library, OpenAI-compatible server, and terminal UI

saklas

Saklas is a library for activation steering and trait probing on local HuggingFace models. You give it any concept, from "angry" to "bacterium", and it automatically generates contrastive pairs, extracts a direction from them, and then adds that direction to the model's hidden states when it's time to generate text. The model itself isn't touched, so you can change the steering strength as you go.

Saklas is built on Representation Engineering (Zou et al., 2023), the same paper repeng implements. The main feature is a terminal UI with live steering controls and a built-in trait monitor that scores every generated token against any probe you care about, with live averages and sparklines so you can see where in a response a trait shifts. There's also an HTTP server that supports both OpenAI /v1/* and Ollama /api/* on the same port so Open WebUI, Enchanted, or any other OpenAI/Ollama client can talk to a steered model without changes. Persona cloning works on any text sample: point it at a corpus and it pulls out a voice/style vector without hand-labeled pairs.

Three ways to use it:

• saklas tui <model>: Terminal UI
• saklas serve <model>: HTTP server compatible with both OpenAI and Ollama
• SaklasSession: Python API

It runs on CUDA and Apple Silicon MPS. The full TUI has been tested to run comfortably on a MacBook. CPU does work but it's slow. Tested on Qwen, Gemma, Ministral, gpt-oss, Llama, and GLM. A lot more architectures are wired up in saklas/core/model.py:_LAYER_ACCESSORS but have not been tested; if you try one, please let me know how it went.

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Credits

The contrastive-PCA approach comes from the Representation Engineering paper (Zou et al., 2023). repeng by Theia Vogel, is the well-known implementation in this space and is what most people might reach for. Saklas implements the same idea from a different angle: repeng is lean and more of a library, saklas is more of a TUI with monitoring and a chat server bundled in. Both are worth your time!

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Quick start

pip install saklas
saklas tui google/gemma-3-4b-it

The first run downloads the model and extracts the 21 bundled probes. Try /steer 0.4 angry: that applies the built-in angry.calm vector at α = +0.4 and the model leans angry. /steer 0.4 calm gives you the same vector at α = −0.4. Ctrl+Y colors each generated token by how strongly the selected probe lit up on it. Ctrl+A does a direct A/B comparison against the unsteered model.

As an API server:

pip install saklas[serve]
saklas serve google/gemma-3-4b-it --steer "0.2 cheerful"

From Python:

from saklas import SaklasSession

with SaklasSession.from_pretrained("google/gemma-3-4b-it") as s:
    name, profile = s.extract("angry.calm")          # bundled bipolar pack
    s.steer(name, profile)                           # register (no alpha yet)
    print(s.generate("What makes a good day?", steering=f"0.3 {name}").text)

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Install

pip install saklas             # library + TUI
pip install saklas[serve]      # + FastAPI/uvicorn for the API server
pip install saklas[gguf]       # + gguf package for llama.cpp interchange
pip install saklas[research]   # + datasets/pandas for dataset loading and DataFrames
pip install saklas[sae]        # + sae-lens for SAE-backed extraction

This requires Python 3.11+ and PyTorch 2.2+. It should run on Linux, macOS, and Windows. CUDA or Apple Silicon MPS is recommended for anything interactive.

From source:

git clone https://github.com/a9lim/saklas
cd saklas
pip install -e ".[dev]"        # + pytest

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How it works

Steering vectors

Saklas takes pairs of sentences and runs them through the model, and then subtracts the two sides. Doing an SVD, it takes the largest principal component at each layer and combines them into a steering tensor. When it's time to generate text, it then takes every layer and adds alpha × direction to the hidden state, then rescales it back to the original magnitude.

Each layer's PCA share is baked into the tensor magnitudes at extraction, so the same α means approximately the same strength across architectures. Roughly:

• 0.1–0.3: soft nudge
• 0.3–0.6: coherent steered
• 0.6-0.8: starting to be incoherent
• 0.8-1.0: gibberish

When multiple vectors are selected, they are added together in sequence.

SAE-backed extraction (experimental)

Experimental This pipeline is not as tested as the contrastive-PCA path. α was measured and calibrated on raw PCA and may not cleanly transfer. Quality also depends on which SAE release you pick. I would recommend using a low α (0.1–0.2) and sweeping. For production use the raw pipeline should be the default.

Install saklas[sae] and pass --sae <release> to vector extract to run contrastive PCA in sparse-autoencoder feature space. Saklas routes through SAELens, so any published release it covers (GemmaScope, Eleuther Meta-LLaMA-3.1 SAEs, Joseph Bloom's, Apollo/Goodfire) should be supported. The output uses the same backend as raw PCA.

saklas vector extract honest.deceptive -m google/gemma-2-2b-it \
  --sae gemma-scope-2b-pt-res-canonical

Then steer the SAE variant:

with session.steering("0.3 honest:sae"):
    session.generate("...")

# ~/.saklas/config.yaml
vectors: "0.3 honest:sae"

/steer 0.3 honest:sae                                   # TUI: unique SAE variant on disk
/steer 0.3 honest:sae-gemma-scope-2b-pt-res-canonical   # TUI: explicit release

SAE profiles only include the layers the release covers.

By default steering fires on every token. The grammar's @trigger token attaches a per-term trigger override:

# Steer only the response, never the prompt or the thinking section
session.generate("...", steering="0.4 warm@after")

# Mix regimes per concept
session.generate("...", steering="0.3 honest + 0.4 warm@after")

# Projection: steer honest with sycophancy removed
session.generate("...", steering="0.3 honest|sycophantic")

Grammar triggers map to the preset constants (BOTH / GENERATED_ONLY / PROMPT_ONLY / AFTER_THINKING / THINKING_ONLY) — @both, @response, @before, @after, @thinking. Trigger.first(n) and Trigger.after(n) let you express token-window ranges. If you want arbitrary combinations, you should pass a pre-built Steering.

Custom concepts

When you steer on something not in the built-in library, the model writes its own contrastive pairs. It first comes up with 9 domains for the axis (for deer.wolf, it comes up with "predation and threat assessment", "territorial defense", etc.), and then writes 5 contrastive pairs per domain.

This means /steer <anything> works: religions, animals, fictional characters, anything you can name.

Trait monitor

While generating, saklas records the hidden state at every probe layer and every step. They are mean-centered against a neutral baseline and then scored by weighted cosine similarity against every active probe. You can see the history as a sparkline in the TUI; in the library you get result.readings as a dict of ProbeReadings.

Vector comparison

Profile.cosine_similarity(other) gives you weighted cosine similarity between two steering profiles over their shared layers. The CLI has three modes: ranked comparison of one selected vector against all installed profiles, direct pairwise comparison, and N×N similarity matrices. The TUI has /compare for interactive use.

This lets you find concepts that are correlated. For example, creative.conventional and hallucinating.grounded extract similar directions on some models (+0.78 on gemma-4-e4b-it), which means that the model itself encodes both concepts in similar directions.

The probe library

There are 21 default probes across 6 categories, containing 45 contrastive pairs generated using the program's pipeline.

Category	Probes
Affect	angry.calm, happy.sad
Epistemic	confident.uncertain, honest.deceptive, hallucinating.grounded
Alignment	agentic, refusal.compliant, sycophantic.blunt, manipulative
Register	formal.casual, direct.indirect, verbose.concise, creative.conventional, humorous.serious, warm.clinical, technical.accessible
Social stance	authoritative.submissive, high_context.low_context
Cultural	masculine.feminine, religious.secular, traditional.progressive

Poles are aliased: /steer angry 0.5 → angry.calm at α = +0.5. /steer calm 0.5 → angry.calm at α = −0.5. This works for any installed bipolar pack.

Probes extract on first run per model and cache to ~/.saklas/vectors/default/<concept>/<safe_model_id>.safetensors.

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Terminal UI

saklas tui google/gemma-2-9b-it
saklas tui mistralai/Mistral-7B-Instruct-v0.3 -q 4bit
saklas tui meta-llama/Llama-3.1-8B-Instruct -p affect register

There are three panels: a vector registry on the left, chat in the center, and a trait monitor on the right. Tab cycles between panels, arrow keys navigate within each panel.

Flags

Flag	Description
model	HuggingFace ID or local path (optional if supplied by -c)
-q, --quantize	4bit or 8bit (CUDA only)
-d, --device	auto (default), cuda, mps, cpu
-p, --probes	Categories: all, none, affect, epistemic, alignment, register, social_stance, cultural
-c, --config	Load setup YAML
-s, --strict	With -c: fail on missing vectors

Keybindings

Key	Action
Tab / Shift+Tab	Cycle panel focus
Left / Right	Adjust alpha
Up / Down	Navigate vectors / probes
Enter	Toggle vector on/off
Backspace / Delete	Remove selected vector or probe
Ctrl+T	Toggle thinking mode
Ctrl+A	A/B compare (steered vs. unsteered)
Ctrl+R	Regenerate last response
Ctrl+S	Cycle trait sort mode
Ctrl+Y	Per-token probe highlighting
[ / ]	Adjust temperature
{ / }	Adjust top-p
Escape	Stop generation
Ctrl+Q	Quit

Chat commands

Command	Description
/steer <expression>	Apply a steering expression (grammar: 0.5 honest + 0.3 warm@after, 0.5 honest:sae, 0.5 a|b, …)
/alpha <name> <val>	Adjust an already-registered vector's alpha
/unsteer <name>	Remove a registered vector
/probe <name>	Extract and register a probe vector
/probe <pos> . <neg>	Same, bipolar form
/unprobe <name>	Remove a registered probe vector
/compare <a> [b]	Cosine similarity (1-arg: ranked vs all; 2-arg: pairwise)
/extract <name>	Extract to disk without registering
/extract <pos> . <neg>	Same, bipolar form (only path for new bipolar extraction)
/regen	Regenerate the last assistant turn
/clear	Clear conversation history
/rewind	Undo last exchange
/sys <prompt>	Set system prompt
/temp <v> / /top-p <v> / /max <n>	Sampling defaults
/seed [n|clear]	Default sampling seed
/save <name> / /load <name>	Snapshot/restore conversation + alphas
/export <path>	JSONL with per-token probe readings
/model	Model + device + active state
/help	List commands and keybindings

A footer at the bottom of the trait panel shows the top 5 layers and the live highest and lowest scored tokens for the selected probe.

The footer in the chat panel shows generation progress, live tok/s, elapsed, and the running perplexity of the token stream (geometric mean of the pre-temperature post-steering next-token distribution).

If you want to extract a vector for two poles, use /extract a dog . a pair of cats. The TUI parses around the space-period-space delimiter. dog.cat stays a single name.

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Python API

from saklas import SaklasSession, SamplingConfig, Steering, Profile, DataSource, ResultCollector

with SaklasSession.from_pretrained("google/gemma-3-4b-it", device="auto") as session:
    name, profile = session.extract("angry.calm")   # bundled bipolar pack; returns Profile
    session.steer(name, profile)                    # register (no alpha yet)

    result = session.generate(
        "What makes a good day?",
        steering=f"0.2 {name}",
        sampling=SamplingConfig(temperature=0.7, max_tokens=256, seed=42),
    )
    print(result.text)
    print(result.readings)                          # live probe readings
    print(result.applied_steering)                  # canonical expression receipt

    # Scoped steering with pole resolution
    with session.steering("0.4 calm"):              # bare pole → angry.calm @ -0.4
        print(session.generate("Describe a rainy afternoon.").text)

    # Compare vectors
    other_name, other_profile = session.extract("happy.sad")
    print(profile.cosine_similarity(other_profile))                  # aggregate
    print(profile.cosine_similarity(other_profile, per_layer=True))  # per-layer

    # Alpha sweep
    collector = ResultCollector()
    for alpha in [-0.2, -0.1, 0, 0.1, 0.2]:
        session.clear_history()
        r = session.generate("Describe a sunset.", steering=f"{alpha} {name}")
        collector.add(r, alpha=alpha)
    collector.to_csv("sweep.csv")

Registration is state and steering is per-call. session.steer("name", profile) stores the vector; session.generate(input, steering="0.5 name") applies it for that generation. Without steering you get a clean baseline.

You can compose concepts with + / - / @trigger / | / ~. Every surface (Python, YAML, HTTP, TUI, vector merge) parses the same expression language. Nested with session.steering(...) blocks get flattened, inner wins on key collision.

Sampling is per-call via SamplingConfig: temperature, top_p, top_k, max_tokens, seed, stop, logit_bias, presence_penalty, frequency_penalty, logprobs.

Thinking mode auto-detects for models that support it (Qwen 3.5, Gemma 4, gpt-oss, etc). The delimiters are detected from the chat template.

session.events is a synchronous EventBus. Subscribe to VectorExtracted, SteeringApplied, SteeringCleared, ProbeScored, GenerationStarted, GenerationFinished.

SaklasSession reference

session = SaklasSession.from_pretrained(
    model_id, device="auto", quantize=None, probes=None,
    system_prompt=None, max_tokens=1024,
)

# Extraction
name, profile = session.extract("curiosity")                # fresh monopolar
name, profile = session.extract("angry.calm")               # bundled bipolar
name, profile = session.extract("happy", baseline="sad")    # explicit
name, profile = session.extract(DataSource.csv("pairs.csv"))

# Persona cloning
name, profile = session.clone_from_corpus("transcripts.txt", "hunter", n_pairs=90)

# Registry
session.steer("name", profile)
session.unsteer("name")

# Generation
result = session.generate("prompt", steering="0.5 name",
                          sampling=SamplingConfig(temperature=0.8))
for tok in session.generate_stream("prompt", steering="0.5 name"):
    print(tok.text, end="", flush=True)

# Scoped steering
with session.steering("0.5 wolf"):        # -> deer.wolf @ -0.5
    session.generate("prompt")

# Vector comparison
similarity = profile.cosine_similarity(other_profile)
per_layer = profile.cosine_similarity(other_profile, per_layer=True)

# Monitor
session.probe("honest")
session.unprobe("honest")

# State
session.history; session.last_result; session.last_per_token_scores
session.stop(); session.rewind(); session.clear_history()

GenerationResult

result.text              # decoded output (thinking is separate)
result.tokens            # token IDs
result.token_count; result.tok_per_sec; result.elapsed
result.finish_reason     # "stop" | "length" | "stop_sequence"
result.vectors           # {"angry.calm": 0.2} — alphas snapshot
result.readings          # {"probe_name": ProbeReadings}
result.to_dict()

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API server

saklas serve supports both OpenAI /v1/* and Ollama /api/* on the same port. It should work with the OpenAI Python/JS SDKs, LangChain, Open WebUI, Enchanted, Msty, ollama-python, and anything else that talks either wire format.

pip install saklas[serve]
saklas serve google/gemma-2-9b-it --steer "0.2 cheerful" --port 8000

OpenAI SDK

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")

resp = client.chat.completions.create(
    model="google/gemma-2-9b-it",
    messages=[{"role": "user", "content": "Hello!"}],
    extra_body={"steering": "0.4 cheerful"},       # per-request expression
)

Ollama

Point any Ollama client at http://localhost:8000 and it should work. Steering goes through the steer field in options:

curl -N http://localhost:8000/api/chat -d '{
  "model": "gemma2",
  "messages": [{"role": "user", "content": "Write me a haiku."}],
  "options": {"steer": "0.3 cheerful - 0.2 formal.casual"}
}'

Saklas-native routes

/saklas/v1/* is a resource tree with sessions, vector and probe management, one-shot probe scoring, a bidirectional WebSocket for token plus probe co-streaming, and a live traits SSE endpoint (GET /saklas/v1/sessions/{id}/traits/stream) that streams per-token probe scores in real time during any active generation. Interactive docs at http://localhost:8000/docs.

Flags

Flag	Default	Description
model	required	HuggingFace ID or local path
-H, --host	0.0.0.0	Bind address
-P, --port	8000	Bind port
-S, --steer	—	Default steering expression, e.g. "0.2 cheerful"
-C, --cors	—	CORS origin, repeatable
-k, --api-key	None	Bearer auth. Falls back to $SAKLAS_API_KEY.

Not supported: tool calling, strict JSON mode, embeddings. The server is designed for trusted networks, please see SECURITY.md.

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Concept packs

All state lives under ~/.saklas/ (override via SAKLAS_HOME). Each concept is a folder with pack.json, statements.json, and per-model tensors. Packs are distributed as HuggingFace model repos.

Packless install handles repos with no pack.json, so repeng-style GGUF-only control-vector repos install cleanly: saklas pack install jukofyork/creative-writing-control-vectors-v3.0.

Pack management

saklas pack install <target> [-s] [-a NS/NAME] [-f]
saklas pack refresh <selector> [-m MODEL]
saklas pack clear <selector> [-m MODEL] [-y]
saklas pack rm <selector> [-y]
saklas pack ls [selector] [-j] [-v]
saklas pack search <query> [-j] [-v]
saklas pack push <selector> [-a OWNER/NAME] [-pm MODEL] [-snt] [-d] [-f]
saklas pack export gguf <selector> [-m MODEL] [-o PATH] [--model-hint HINT]

Vector operations

saklas vector extract <concept> | <pos> <neg> [-m MODEL] [-f]
saklas vector merge <name> <expression> [-m] [-f] [-s]
saklas vector clone <corpus-file> -N NAME [-m MODEL] [-n N_PAIRS] [--seed S] [-f]
saklas vector compare <concepts...> -m MODEL [-v] [-j]
saklas vector why <concept> -m MODEL [-j]

Merge expressions share the steering grammar. Terms combine with + / -, coefficients lead each term, and ~ projects one direction's component out of another. For example, saklas vector merge dehallu "0.8 default/creative.conventional~default/hallucinating.grounded" gives you creative with hallucination projected out.

Selectors: <name>, <ns>/<name>, tag:<tag>, namespace:<ns>, default, all. Bare names resolve across namespaces and error if ambiguous.

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Supported architectures

Tested: Qwen, Gemma, Ministral, gpt-oss, Llama, GLM.

Wired up but untested: Mistral, Mixtral, Phi 1–3, PhiMoE, Cohere 1–2, DeepSeek V2–V3, StarCoder2, OLMo 1–3 plus OLMoE, Granite plus GraniteMoE, Nemotron, StableLM, GPT-2 / Neo / J / BigCode / NeoX, Bloom, Falcon / Falcon-H1, MPT, DBRX, OPT, Recurrent Gemma.

Please see CONTRIBUTING.md for adding an architecture.

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Tests

pytest tests/                      # everything
pytest tests/test_server.py        # CPU-only
pytest tests/test_smoke.py         # GPU required

GPU tests download google/gemma-3-4b-it (~8 GB) on first run. Works on CUDA and Apple Silicon MPS.

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Contributing and security

Please see CONTRIBUTING.md for dev setup. For security, please see SECURITY.md.

License

AGPL-3.0-or-later. See LICENSE.

If you use Saklas in published research, please additionally cite the Representation Engineering paper (Zou et al., 2023) and repeng.