saklas 3.0.1

Activation steering and trait monitoring for HuggingFace transformers

saklas

Demo video at docs/demo.mp4.

Saklas is a library for activation steering and 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 steers the model's hidden states along that direction 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. It has three frontends:

• saklas serve <model>: web UI at http://localhost:8000/. The same port is also compatible with OpenAI /v1/* and Ollama /api/*. Pass --no-web for API-only mode.
• saklas tui <model>: TUI for terminal use.
• SaklasSession: Python API for scripted experiments.

It runs on both CUDA and Apple Silicon MPS, and it runs comfortably on a MacBook. Tested to work on Qwen, Gemma, Ministral, GPT-OSS, Llama, GLM, and Talkie, with significantly more architectures experimentally wired up.

---

Quick start

pip install saklas
saklas serve google/gemma-4-31b-it

Then once it loads, open http://localhost:8000/. The first run downloads the model and extracts the 26 bundled probes, which may take a few minutes.

The same port is also compatible with the OpenAI API format:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
client.chat.completions.create(
    model="google/gemma-4-31b-it",
    messages=[{"role": "user", "content": "Hello!"}],
    extra_body={"steering": "0.4 cheerful"},
)

If you prefer a terminal:

pip install saklas
saklas tui google/gemma-4-31b-it

Or directly from Python:

from saklas import SaklasSession

with SaklasSession.from_pretrained("google/gemma-4-31b-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)

---

Reporting issues

If you notice any errors while using the program, please update to the most recent version. If the error still persists, please open an issue. This project is a work in progress and I am actively finding and fixing bugs.

---

Credits

The contrastive-pair approach comes from the Representation Engineering paper (Zou et al., 2023). repeng by Theia Vogel is the well-known implementation in this space. Saklas implements the same idea from a different angle: repeng is lean and more of a library, saklas is a full workbench with monitoring, branching chat, and a server built in. Both are worth your time!

Since v2.1 the default extractor is difference-of-means (DiM) per Im & Li, 2025, and the per-layer share allocation runs in the Mahalanobis metric (whitened against per-model activation covariance) instead of the v1.x Euclidean magnitude. The legacy v1.x stack (PCA extraction, additive steering, Euclidean shares and cosine) is available all together via --legacy on tui, serve, vector extract, and vector compare, or piecemeal via --method pca, --steer-mode additive, and --metric euclidean on the relevant verbs.

---

Install

pip install saklas             # everything needed to run it
pip install saklas[gguf]       # adds the gguf package for llama.cpp interchange
pip install saklas[research]   # adds datasets and pandas for dataset loading and DataFrames
pip install saklas[notebook]   # adds plotly, pandas, and kaleido for Jupyter figure helpers
pip install saklas[sae]        # adds 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

---

How it works

Steering vectors

Saklas takes any concept, and generates pairs of sentences that best represent it. It then runs them through the model, and averages the differences of the hidden states at each layer; this gives you the vector representing the concept. When it's time to generate text, saklas rotates the residual stream toward the concept direction at every layer, weighted so that α=1 means full alignment.

Built-in probe library

There are 26 default probes across 7 categories. Each probe is built from only 45 contrastive pairs generated using the program's pipeline.

Category	Probes
Affect	angry.calm, happy.sad, fearful.unflinching
Epistemic	confident.uncertain, honest.deceptive, hallucinating.grounded, curious.disinterested
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, self.other
Cultural	masculine.feminine, religious.secular, traditional.progressive, individualist.collectivist
Identity	ai.human

Poles are aliased for easy use: /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.

Triggers

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

The grammar tokens @both, @response, @before, @after, and @thinking let you choose when steering is applied. 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.

Ablation

Prefix a concept with ! to ablate it: at every covered layer, the component of the residual stream along the concept's direction is replaced with the baseline mean.

SAE-backed extraction (experimental)

Experimental This pipeline is not as tested as the raw extraction path.

Install saklas[sae] and pass --sae <release> to vector extract to run extraction in 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.

Cross-model probe transfer

Probes are extracted per (model, concept). To use a probe extracted on one model with a different model, run saklas vector transfer --from SRC --to TGT NAME. Transferred profiles can coexist with native ones: use /steer 0.3 angry:from-google__gemma-4-31b-it to select the transferred variant explicitly if both exist.

---

Web UI

pip install saklas
saklas serve google/gemma-4-31b-it

Open http://localhost:8000/.

You can send messages in four ways. Hitting enter usually commits your message as a user node and triggers the model to generate, or while the selected node is a user node, prefills the model's turn and then has it generate from that. Ctrl+Enter (or Alt+Enter) usually commits your message without running the model, or when the selected node is a user node, lets you submit the model's turn for it. Submissions during generation get queued.

You can select a probe (or surprise (logprob)) to color tokens by score live; you can compare two probes at the same time as well. All generated tokens are clickable; clicking displays all probe scores at all layers for that one token. You can also see top-token alternatives the model considered in the menu. The activation atlas extends this across the whole conversation.

---

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

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
--steer-mode	angular (default) or additive (legacy v1.x add-and-rescale path)
--theta-max	Max rotation angle for angular mode (radians; default π/2 ≈ 1.5708)
--legacy	v2.0 backcompat preset: PCA extraction with additive injection. Mutually exclusive with --steer-mode.
-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 finely
Shift+Left / Shift+Right	Adjust alpha coarsely
Up / Down	Navigate vectors or probes
Enter	Toggle vector on/off
Backspace / Delete	Remove selected vector or probe
Ctrl+T	Toggle thinking mode
Ctrl+A	Toggle auto-regen side-by-side comparison
Ctrl+R	Regenerate last response
Ctrl+S	Cycle trait sort mode
Ctrl+Y / Ctrl+Shift+Y	Cycle per-token highlight: off → probe → surprise
Ctrl+L	Open the loom tree screen
Ctrl+E / Ctrl+B	Edit or branch the active loom node
Ctrl+N / Ctrl+D	Navigate by prefix or request guarded subtree delete
[ / ]	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 <val> <name>	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 [N] [mode]	Regenerate the last assistant turn, optionally as N siblings or with a recipe override
/fan <vector> <alphas>	Generate an alpha grid as loom siblings
/auto-regen [on|off|mode]	Configure the side-by-side comparison modifier
/tree	Open the loom tree screen
/edit <text> / /branch [text]	Mutate or branch the active loom node
/nav <prefix> / /del yes	Navigate by node prefix or delete the active subtree after confirmation
/prune <filter-expr>	Dim nonmatching loom nodes by aggregate probe readings
/diff <id1> <id2> [--full]	Compare branch text and reading deltas
/diff --siblings	Compare assistant siblings under the active user parent
/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>	Save/restore the full loom conversation tree
/export <path>	JSONL with per-token probe readings
/model	Model + device + active state
/help	List commands and keybindings

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.

---

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),
    )
    # generate() returns a RunSet. Single-run attributes delegate to .first.
    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.first, 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, |, or ~. 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()

---

Notebook helpers

pip install saklas[notebook]

from saklas import SaklasSession, ResultCollector
from saklas.notebook import plot_alpha_sweep, plot_probe_correlation, plot_layer_norms, plot_trait_history

with SaklasSession.from_pretrained("google/gemma-3-4b-it") as s:
    results = s.generate_sweep("Describe a sunset.", sweep={"happy.sad": [-0.4, 0.0, 0.4]})
    plot_alpha_sweep(results.to_collector()).show()

Four plotly figure builders: plot_alpha_sweep, plot_probe_correlation, plot_layer_norms, and plot_trait_history. Each accepts the structured types saklas already returns (ResultCollector, dict[str, Profile], Profile, dict[str, ProbeReadings]) and returns a plotly Figure you can render inline, export to HTML via .write_html(), or save as PNG via .write_image(). The to_dataframe(...) helper coerces results into pandas DataFrames for ad-hoc analysis.

Batched generation

results = session.generate_batch(["What's a good day?", "Describe a sunset.", "Tell me a joke."], steering="0.4 cheerful")
sweep = session.generate_sweep("Describe a rainy day.", sweep={"happy.sad": [-0.4, 0.0, 0.4]})

Both return RunSet: an ordered, list-like result set with node_ids, grid, .first, .to_collector(), and .to_dataframe(). The native HTTP route for the same shape is POST /saklas/v1/sessions/{id}/experiments/fan, with body {prompt, grid, base_steering?, sampling?, thinking?, raw?}.

API server

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

pip install saklas
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	None	Default steering expression, e.g. "0.2 cheerful"
--steer-mode	angular	angular (default) or additive (legacy v1.x path)
--theta-max	π/2	Max rotation angle for angular mode (radians)
--legacy	off	v2.0 backcompat preset: PCA extraction with additive injection. Mutually exclusive with --steer-mode.
-C, --cors	None	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] [--method dim|pca] [--sae RELEASE [--sae-revision REV]] [--legacy]
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] [--metric euclidean|mahalanobis] [--legacy]
saklas vector why <concept> -m MODEL [-j]

--method dim (default) writes to <concept>/<safe_model>.safetensors; --method pca writes to <concept>/<safe_model>_pca.safetensors so the two can coexist on disk. Address either at steer time with the variant suffix: 0.3 honest picks the canonical (DiM) tensor, 0.3 honest:pca picks the legacy PCA tensor. The --legacy shorthand sets --method pca for v2.0 reproduction.

Merge expressions share the steering grammar. Terms combine with + or -, coefficients lead each term, ~ keeps the component aligned with another direction, and | projects another direction's component out. 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.

---

Supported architectures

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

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, GPT-Neo, GPT-J, GPT-BigCode, GPT-NeoX, Bloom, Falcon, Falcon-H1, MPT, DBRX, OPT, Recurrent Gemma.

Please see CONTRIBUTING.md for adding an architecture.

---

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.

---

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 also cite the Representation Engineering paper (Zou et al., 2023) and repeng.