OkeyMetaLtd/SLE-V2.1-Omega12K-LX

SLE-V2.1-Ω12K-LX

Saccadic is a deployable Cognitive Resonance Artifact from OkeyMeta Ltd. It is built for people who want to run a portable AI system, connect their own tools, and host it anywhere without cloning the private architecture repository. No private repository checkout is required.

Mission

SLE is a CPU-first path toward artifact-native intelligence: learned state that can move across machines, run close to users, and use external tools without turning the host application into a hidden model. The goal is simple for builders: ship model.cra, start Spark, and let Saccadic expose what it selected, argued, remembered, and generated from loaded artifact state.

Saccadic Highlights

• SLE replaces parameter-count thinking with portable cognitive state. The artifact is the model.
• No tokenizer. No Transformer stack. No private repo checkout.
• model.cra carries learned state: memory, liquid ODE coefficients, instruction surfaces, response policy, speech transitions, stream state, facts, and tool/action state.
• Spark is a runtime boundary, not a hidden second model. It loads the artifact, executes declared modes, and returns loaded-state evidence.
• Raw text and raw acoustic input are processed through predictive stream dynamics instead of BPE tokenization or speech-to-text.
• Host tools are executable boundaries. Saccadic selects learned tool paths, emits arguments, consumes returned values, and speaks through artifact state.
• SLE names releases by cognitive capacity notation, not parameter count.

Saccadic is designed for personal assistants, workflow agents, private services, edge systems, desktop apps, and embedded hosts that need a portable AI boundary.

What You Can Build

• Conversational assistants that carry recent chat history into the artifact runner.
• Workflow agents that select learned tool names and emit auditable argument maps.
• Private customer, operations, research, or device copilots that keep host services as executable boundaries.
• Edge, desktop, and container deployments where model.cra and Spark move together.
• OpenAI-compatible chat services for teams that already use SDK-based application code.

Model Overview

Field	Value
Release	SLE-V2.1-Ω12K-LX
Hugging Face slug	SLE-V2.1-Omega12K-LX
Model name	Saccadic
Architecture	Saccadic-Liquid Engine (SLE)
Public artifact	model.cra
Spark runtime	sle_spark.py
Metrics	sle_v2_1_release_metrics.json
Requirements	requirements.txt
Selected training rows	502,000
Curation failures	0
Deployment targets	cli, service, edge, desktop, container

This package includes model.cra, Spark, requirements metadata, release metrics, the release manifest, and generated host boundaries. The private source repository is not required to run Saccadic.

Quickstart

Download this Hugging Face model package and keep the files together. The public runtime is OpenAI SDK-compatible: start Spark, point the SDK at the local service, and call client.chat.completions.create. Spark exposes POST /v1/chat/completions behind the local /v1 base URL.

python -m pip install -r requirements.txt
python -m pip install openai
python .\sle_spark.py serve-artifact --artifact model.cra --host 127.0.0.1 --port 8765

Create one chat.py beside model.cra and sle_spark.py:

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:8765/v1",
    api_key="not-needed",
)

messages = [
    {"role": "system", "content": "Reply naturally and use the latest user turn."},
    {"role": "user", "content": "Hello! Tell me about yourself."},
]

stream = client.chat.completions.create(
    model='SLE-V2.1-Ω12K-LX',
    messages=messages,
    stream=True,
    extra_body={"max_speech_steps": 18},
)

answer = []
evidence = {}
for event in stream:
    piece = event.choices[0].delta.content or ""
    print(piece, end="")
    answer.append(piece)
    extra = getattr(event, "model_extra", {}) or {}
    evidence = extra.get("saccadic", evidence)
print()

messages.append({"role": "assistant", "content": "".join(answer)})
print("tool path:", evidence.get("tool_path"))
print("final value:", evidence.get("final_value"))

Expected shape: normal streamed chat text first, then optional loaded-artifact evidence. For arithmetic, tools, facts, and system-instruction turns, the saccadic evidence shows selected tool paths, emitted arguments, final values, slot transfers, and replay evidence.

To bind your own tool, keep the same OpenAI SDK call and add extra_body={"host_tools": ["artifact.tool_name=your_module:your_function"]}. Spark executes that callable only if model.cra selects the learned tool name and emits arguments from artifact state.

Deployment

• HF repo slug: SLE-V2.1-Omega12K-LX
• The release name remains SLE-V2.1-Ω12K-LX.
• Declared deployment targets: cli, service, edge, desktop, container.
• Do not upload the source repository to run Saccadic.
• Spark runtime can be embedded behind CLI, service, edge, desktop, or container hosts.
• Deployments may bind OS, network, database, browser, robotics, or private business tools by learned artifact tool name.

Deployment Entry Points

• cli: ./sle_spark.py run-artifact --artifact model.cra
• service: ./sle_spark.py serve-artifact --artifact model.cra --host 0.0.0.0 --port 8000
• edge: python edge_host.py
• desktop: python desktop_host.py
• container: Containerfile

Included Host Boundaries

• edge: edge_host.py
• desktop: desktop_host.py
• container: Containerfile

Install

Place model.cra, sle_spark.py, SLE_RELEASE.json, and any declared metrics or requirements metadata in one directory. Then run python -m pip install -r requirements.txt from that directory. The runtime command below is the public boundary; users do not install this private repository.

Direct CLI

Most applications should use the OpenAI-compatible service above. The direct CLI remains available for diagnostics and returns the same loaded-artifact evidence from model.cra.

Edge And Desktop

Use the generated host files when included:

python edge_host.py
python desktop_host.py

Both read JSON payloads from stdin, forward declared host tools to the shipped runtime, and print loaded-artifact evidence.

Raw Acoustic Input

Raw acoustic runs pass waveform samples directly; there is no speech-to-text or tokenizer layer:

python .\sle_spark.py run-artifact --artifact model.cra --mode acoustic --audio-samples "[0.0,0.17,0.34,0.17,0.0,-0.13,-0.30,-0.13,0.0]" --audio-symbol-score-floor 0.25 --diverse-speech-limit 2

Best Practices

• Keep model.cra, Spark, requirements.txt, SLE_RELEASE.json, and any metrics files in the same deployment directory.
• Use --avoid-replay for public demos so exact source-row echoes are surfaced as evidence instead of mistaken for intelligence.
• Bind host tools with --host-tool artifact.name=module:function; the host executes tools, while Saccadic selects paths and emits arguments from loaded artifact state.
• Spark includes documented portable primitives for math.add and math.power; external tools still use explicit host bindings.
• Pass recent conversation as raw text in history mode when you want Saccadic to respond to prior turns.
• Inspect returned JSON fields such as selected tool paths, emitted arguments, final values, stream dynamics, punctuation symbols, slot transfers, and replay evidence.

Trust And Evidence

Saccadic is an artifact-first release: supported response, history, autonomous, action, acoustic, and system-instruction paths return loaded-state evidence rather than hidden host-written answers. Evidence includes tool paths, emitted arguments, final values, autonomous route records, context transfers, punctuation symbols, Speech Cortex usage counts, system-instruction records, fact attributes, slot transfers, emitted exact-row replay evidence, and blocked replay evidence.

Training And Selection

• Selected rows: 502,000
• Curation failures: 0

Training Domains

• arts: 20,080
• biology: 20,080
• chemistry: 20,080
• constitution: 20,080
• conversation: 20,080
• economics: 20,080
• emoji: 20,080
• geography: 20,080
• history: 20,080
• language_es: 20,080
• language_fr: 20,080
• language_ha: 20,080
• language_ig: 20,080
• language_yo: 20,080
• language_zh: 20,080
• law: 20,080
• math: 20,080
• medicine: 20,080
• philosophy: 20,080
• physics: 20,080
• safety: 20,080
• stories: 20,080
• system_instruction: 20,080
• technology: 20,080
• world: 20,080

Citation

@software{sle_saccadic,
  title = {Saccadic-Liquid Engine: Saccadic Cognitive Resonance Artifact},
  author = {OkeyMeta Ltd},
  version = {SLE-V2.1-Ω12K-LX},
  note = {CPU-first Cognitive Resonance Artifact with Spark runtime}
}