Instructions to use THARX/THAR.0X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use THARX/THAR.0X with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="THARX/THAR.0X",
	filename="THAR.0X-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is undefined.\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use THARX/THAR.0X with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf THARX/THAR.0X:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf THARX/THAR.0X:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf THARX/THAR.0X:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf THARX/THAR.0X: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 THARX/THAR.0X:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf THARX/THAR.0X: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 THARX/THAR.0X:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf THARX/THAR.0X:Q4_K_M

Use Docker

docker model run hf.co/THARX/THAR.0X:Q4_K_M

LM Studio
Jan
Ollama
How to use THARX/THAR.0X with Ollama:
```
ollama run hf.co/THARX/THAR.0X:Q4_K_M
```

Unsloth Studio

How to use THARX/THAR.0X 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 THARX/THAR.0X 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 THARX/THAR.0X to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for THARX/THAR.0X to start chatting

How to use THARX/THAR.0X with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf THARX/THAR.0X: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": "THARX/THAR.0X:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use THARX/THAR.0X with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf THARX/THAR.0X: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 THARX/THAR.0X:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use THARX/THAR.0X with Docker Model Runner:
```
docker model run hf.co/THARX/THAR.0X:Q4_K_M
```

Lemonade

How to use THARX/THAR.0X with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull THARX/THAR.0X:Q4_K_M

Run and chat with the model

lemonade run user.THAR.0X-Q4_K_M

List all available models

lemonade list

THARX commited on 15 days ago

Commit

f418470

1 Parent(s): 3067009

docs: add prominent LM Studio / GGUF direct usage guide to top of README

Browse files

Files changed (1) hide show

README.md +15 -0

README.md CHANGED Viewed

@@ -16,6 +16,21 @@ THAR.0X is a cognitive architecture — not a single fine-tuned model, but a sys
 engineered from the analysis of 12 different model architectures to activate capabilities
 in any capable base LLM and produce behaviour that exceeds any individual fine-tune.
 ---
 ## Quick Summary

 engineered from the analysis of 12 different model architectures to activate capabilities
 in any capable base LLM and produce behaviour that exceeds any individual fine-tune.
+---
+> [!IMPORTANT]
+> **Running in LM Studio / GGUF Applications:**
+> Since THAR.0X is a **cognitive architecture** (system prompt persona + configuration) rather than a raw weights file, it does not download as a standalone `.gguf` file.
+>
+> To run THAR.0X in **LM Studio** instantly:
+> 1. Download or load any capable base model (e.g., Llama 3.2 or Qwen 2.5).
+> 2. Copy the contents of the [system_prompt.txt](system_prompt.txt) file in this repository.
+> 3. Paste it into the **System Prompt** field in your Chat window.
+> 4. Set the **Temperature** parameter to `0.85` in the Right Panel.
+>
+> You are now chatting with the live THAR.0X persona.
 ---
 ## Quick Summary