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

Libraries

How to use webslug/cogent with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="webslug/cogent",
	filename="alive_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use webslug/cogent with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio

How to use webslug/cogent 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 webslug/cogent 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 webslug/cogent to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for webslug/cogent to start chatting

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-server -hf webslug/cogent:Q4_K_M

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": "webslug/cogent:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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 new
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

YAML Metadata Warning:empty or missing yaml metadata in repo card

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
---

Downloads last month: 147

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

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