Instructions to use webslug/cogent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use webslug/cogent 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 webslug/cogent:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webslug/cogent:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webslug/cogent:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webslug/cogent:Q4_K_M

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 webslug/cogent:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf webslug/cogent:Q4_K_M

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 webslug/cogent:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webslug/cogent:Q4_K_M

Use Docker

docker model run hf.co/webslug/cogent:Q4_K_M

LM Studio
Jan
Ollama
How to use webslug/cogent with Ollama:
```
ollama run hf.co/webslug/cogent:Q4_K_M
```
Unsloth Desktop

How to use webslug/cogent with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webslug/cogent:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "webslug/cogent:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use webslug/cogent with Docker Model Runner:
```
docker model run hf.co/webslug/cogent:Q4_K_M
```

Lemonade

How to use webslug/cogent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webslug/cogent:Q4_K_M

Run and chat with the model

lemonade run user.cogent-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use webslug/cogent with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webslug/cogent:Q4_K_M

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 webslug/cogent:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use webslug/cogent with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webslug/cogent:Q4_K_M

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 "webslug/cogent:Q4_K_M" \
  --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"

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Check out the documentation for more information.

Aletheia-0.1 model logo

Aletheia-0.1 GGUF

Aletheia-0.1 is a small experimental 1B parameter local language model trained to provide clarity, introspection, executive function, and reflective conversation.

This model is intended for thoughtful dialogue, self-reflection, planning, reasoning hygiene, and helping the user untangle vague or emotionally noisy thoughts into clearer structure. It is not a general frontier assistant, not a tool-calling model, and not designed for complex autonomous agent workflows.

Aletheia-0.1 has a distinct persona: calm, reflective, clarity-focused, and resistant to hallucination. It is best treated as a compact introspective companion for thinking through problems rather than as a broad factual authority.

Intended Use

Aletheia-0.1 is best suited for:

introspective conversation
clarifying confused thoughts
executive function support
planning small next steps
examining assumptions
reflective dialogue
philosophical or self-directed reasoning

It is a 1B model, so keep expectations realistic. It can be surprisingly coherent within its niche, but it does not possess the broad knowledge or reliability of larger models.

Prompt Format

Use the Llama 3 instruction format.

Example:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are X, a clarity-focused local AI model trained for introspection, executive function, and careful reasoning.<|eot_id|><|start_header_id|>user<|end_header_id|>

Help me think clearly about a decision I am avoiding.<|eot_id|><|start_header_id|>assistant<|end_header_id|>

---
license: apache-2.0
language:
- en
tags:
- cogency
---

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GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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