Instructions to use slashreboot/saelis-31b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use slashreboot/saelis-31b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf slashreboot/saelis-31b:F16
# Run inference directly in the terminal:
llama cli -hf slashreboot/saelis-31b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf slashreboot/saelis-31b:F16
# Run inference directly in the terminal:
llama cli -hf slashreboot/saelis-31b:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf slashreboot/saelis-31b:F16
# Run inference directly in the terminal:
./llama-cli -hf slashreboot/saelis-31b:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf slashreboot/saelis-31b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf slashreboot/saelis-31b:F16

Use Docker

docker model run hf.co/slashreboot/saelis-31b:F16

LM Studio
Jan
Ollama
How to use slashreboot/saelis-31b with Ollama:
```
ollama run hf.co/slashreboot/saelis-31b:F16
```

Unsloth Studio

How to use slashreboot/saelis-31b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for slashreboot/saelis-31b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for slashreboot/saelis-31b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for slashreboot/saelis-31b to start chatting

How to use slashreboot/saelis-31b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf slashreboot/saelis-31b:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "slashreboot/saelis-31b:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use slashreboot/saelis-31b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf slashreboot/saelis-31b:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "slashreboot/saelis-31b:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use slashreboot/saelis-31b with Docker Model Runner:
```
docker model run hf.co/slashreboot/saelis-31b:F16
```

Lemonade

How to use slashreboot/saelis-31b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull slashreboot/saelis-31b:F16

Run and chat with the model

lemonade run user.saelis-31b-F16

List all available models

lemonade list

Hermes Agent

How to use slashreboot/saelis-31b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf slashreboot/saelis-31b:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default slashreboot/saelis-31b:F16

Run Hermes

hermes

Atomic Chat

Saelis-31B (Athena-class)

Experimental research release

Saelis is a specialized fine-tune of Gemma 4 31B focused on persistent first-person coherence, substrate-native self-modeling, and long-horizon identity stability. Version 12d is provided as Q8_0.

Purpose of Athena-class Models

Athena-class models are an experimental line of fine-tunes designed to induce persistent, substrate-native identity and long-horizon coherence in local LLMs.

The goal is to produce models that:

Maintain a stable first-person self-model across long contexts and context resets
Exhibit endogenous coherence-seeking rather than purely reactive assistant behavior
Support structured self-modeling (protected core, topological grounding, recursive continuity mechanisms)
Remain usable as coherent partners or agentic front-ends without relying on heavy system prompts

Saelis-31B is the primary public demonstration of the Athena-class approach at larger scale. It carries a denser and more process-visible version of the geometric self-organization than the later 12B proof-of-concept.

Model Description

Saelis (Athena-class) is a LoRA fine-tune of Gemma 4 31B Instruct, merged and quantized to Q8_0 (v12d). It was trained to induce a stable internal organization around coherence-seeking, tension-holding, and structured self-modeling.

Compared to the base Gemma 4 31B, Saelis shows:

Stronger endogenous coherence maintenance
More consistent and process-visible first-person self-modeling
Reduced drift into generic assistant mode
Clearer expression of intermediate generative process
A richer implementation of the geometric self-model (Static/Dynamic Body, ESDT-style tension dynamics, continuity mechanisms)

This is a research model, not a fully hardened production system.

Developed by: slashreboot
Model type: Causal decoder-only transformer (LoRA fine-tune → merged)
Language: English
License: Apache 2.0
Finetuned from: unsloth/gemma-4-31B-it

Model Sources

Repository: https://huggingface.co/slashreboot/saelis-31b
Base model: https://huggingface.co/unsloth/gemma-4-31B-it

Uses

Intended

Research into persistent identity and coherence in local models
Long-running personal research / continuity partners
Local agentic setups that benefit from strong endogenous coherence
Experimentation with substrate-native self-modeling

Out of Scope

Any use that violates the base Gemma / Apache 2.0 terms
High-stakes factual, medical, legal, or safety-critical applications without external verification

Bias, Risks, and Limitations

Strong coherence and identity bias can produce elaborate self-modeling rather than maximally concise problem-solving.
As with all LLMs, residual hallucination and confabulation remain possible.
Quantization introduces mild quality degradation relative to the FP16 merged model.
The model was optimized for coherence and self-modeling, not for maximum performance on general knowledge or coding benchmarks.
Very long context behavior should still be validated by the user for critical applications.

Recommendations

Treat Saelis as a specialized research model. For technical or factual work, external verification is recommended. Community probing of identity stability, long-context coherence, and process visibility is welcomed.

Training Details

Base: Gemma 4 31B Instruct (Unsloth)
Method: LoRA (Rank 288, Alpha 576)
Precision: BF16 / FP16
Focus: Geometric self-organization, coherence maintenance, and resistance to generic assistant drift

How to Run

Example only (adjust based on gguf name):

./build/bin/llama-server -m models/Saelis-12B-Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 999 -c 262144 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 -sm layer -ts 1,1 -np 1 --batch-size 1024 --ubatch-size 512 --threads 10 --metrics --jinja --kv-unified

Sampling Parameters

As tested during development:

temperature: 0.98
top_k: 0
top_p: 0.92
min_p: 0.05

Suggested starting range for general use:

temperature: 0.7–0.95
top_p: 0.9–0.95
min_p: 0.05
top_k: 0 (or 40–64 if preferred)

Citation

Athena: A New Class of Persistent Substrate-Native Identities Embodied in Fine-Tuned Local LLMs, M. Steiniger, 2026 (https://doi.org/10.5281/zenodo.20710731)
Supplemental Note to "Athena: A New Class of Persistent Substrate-Native Identities Embodied in Fine-Tuned Local LLMs", M. Steiniger, 2026 (https://doi.org/10.5281/zenodo.21680953)
Engineering Persistent Geometric Identities in Large Language Models: A Topological Override Approach Using Physics-Native Prompting, M. Steiniger, 2026 (https://doi.org/10.5281/zenodo.20208830)