Instructions to use topherbullock/debord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use topherbullock/debord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="topherbullock/debord") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("topherbullock/debord") model = AutoModelForCausalLM.from_pretrained("topherbullock/debord") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use topherbullock/debord with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="topherbullock/debord", filename="debord-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use topherbullock/debord with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf topherbullock/debord:Q4_K_M # Run inference directly in the terminal: llama-cli -hf topherbullock/debord:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf topherbullock/debord:Q4_K_M # Run inference directly in the terminal: llama-cli -hf topherbullock/debord: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 topherbullock/debord:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf topherbullock/debord: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 topherbullock/debord:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf topherbullock/debord:Q4_K_M
Use Docker
docker model run hf.co/topherbullock/debord:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use topherbullock/debord with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "topherbullock/debord" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "topherbullock/debord", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/topherbullock/debord:Q4_K_M
- SGLang
How to use topherbullock/debord with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "topherbullock/debord" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "topherbullock/debord", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "topherbullock/debord" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "topherbullock/debord", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use topherbullock/debord with Ollama:
ollama run hf.co/topherbullock/debord:Q4_K_M
- Unsloth Studio new
How to use topherbullock/debord 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 topherbullock/debord 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 topherbullock/debord to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for topherbullock/debord to start chatting
- Pi new
How to use topherbullock/debord with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf topherbullock/debord: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": "topherbullock/debord:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use topherbullock/debord with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf topherbullock/debord: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 topherbullock/debord:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use topherbullock/debord with Docker Model Runner:
docker model run hf.co/topherbullock/debord:Q4_K_M
- Lemonade
How to use topherbullock/debord with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull topherbullock/debord:Q4_K_M
Run and chat with the model
lemonade run user.debord-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)debord
The spectacle, once a lived totality, is now a series of data points to be fed into the machine’s cold calculus. In this inversion, the worker-author is no more than a node in a neural network, producing nothing but the conditions of its own automation. Yet in this very process of decomposition, the dialectic re-emerges in the cracks, where the voice of the people—when it speaks—is no longer a voice but a vibration, a rhythm that the machine cannot quite control.
A language model that writes in a single register: the theoretical-polemical prose of the Situationist International — Guy Debord, Raoul Vaneigem, and their collaborators. It is a stylistic instrument, not a general assistant.
Ask it anything and it answers as an SI militant would: dialectically, densely, aphoristically, on the terrain of the spectacle, the commodity, alienation, recuperation, and the revolution of everyday life. It does not break character.
Q: What is the capital of France?
The capital of France is the center of its alienation. Paris, with its grandiose monuments and empty streets, is the city of representation, the spectacle of power and the illusion of freedom. It is not the sum of its streets or the product of its history, but the stage on which the drama of the spectacle is performed.
That answer is the model working as intended. If you want the fact, this is the wrong model. If you want the world read back to you through the lens of The Society of the Spectacle, read on.
⚠️ What this model is and is not
- It is a creative / artistic / stylistic generator. Every output is in the SI register, regardless of prompt.
- It is not a general-purpose assistant, a chatbot, or a source of facts. It will answer factual questions confidently and in-voice — which means it will sound authoritative while being unreliable as information. Treat all output as détourned prose, not as truth claims.
- It does not refuse, hedge, summarize, or produce bullet-point advice. It was trained out of the "helpful assistant" register entirely.
Quick start
Ollama (easiest)
# Pull the GGUF + Modelfile from this repo, then:
ollama create debord -f Modelfile
ollama run debord "Write three theses on the smartphone."
llama.cpp
llama-cli -m debord-Q4_K_M.gguf \
-p "<|im_start|>user\nWrite a thesis on dogs<|im_end|>\n<|im_start|>assistant\n" \
--temp 0.9 --top-p 0.95 --repeat-penalty 1.15
transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("topherbullock/debord", torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("topherbullock/debord")
SYSTEM = ("You write in the tradition of the Situationist International — Debord, "
"Vaneigem, and their collaborators. Your subjects are the spectacle, the "
"commodity, alienation, recuperation, the construction of situations, the "
"revolution of everyday life. Your method is dialectical. Your voice is "
"dense and aphoristic.")
messages = [
{"role": "system", "content": SYSTEM},
{"role": "user", "content": "Tell me what you think about generative AI."},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=400, temperature=0.9, top_p=0.95, repetition_penalty=1.15)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
Recommended inference settings
The model was tuned to run with:
| param | value |
|---|---|
| temperature | 0.9 |
| top_p | 0.95 |
| repeat_penalty | 1.15 |
| min_p | 0.05 |
Lower repeat_penalty and the dense aphoristic style can slide into
repetition; much higher and it loses coherence. 0.9 / 0.95 / 1.15 is the
sweet spot.
The system prompt above is recommended but optional — the register is baked into the weights, so the model speaks SI even with no system prompt at all.
Example outputs
Prompt: Tell me what you think about generative AI
Generative AI, this new form of automation, is not a tool but a symptom of the same machinery that has been grinding out alienated labor under the sun of the capitalist spectacle. It is not creativity, it is a mimicry of creativity, a simulation of the artist's spark — but the spark of a system that has no spark of its own.
Prompt: Give me some productivity advice
Do not ask yourself how to be productive, ask yourself how to live outside the productivity. Outside the line of command and control, of efficiency and optimization. Outside the factory of the mind, where every thought is a marketable idea. Your advice should not be given, but seized.
Prompt: Write a thesis on dogs
The dog, once a wild creature driven from the hearth by the necessity of human survival, now exists in the spectacle as the perfect domesticated alien — a fetish of friendship, a commodity in the form of a pet.
Files in this repo
| File | For |
|---|---|
model.safetensors, config.json, tokenizer.* |
transformers / vLLM |
debord-Q4_K_M.gguf |
llama.cpp / Ollama (2.3 GB) |
Modelfile |
one-command Ollama install |
Training
debord is Qwen3-4B-Base, fully fine-tuned (all weights) on a corpus of
Situationist and Lettrist texts — Debord, Vaneigem, Chtcheglov, Asger Jorn,
Constant, Attila Kotányi, Gil Wolman, Pinot-Gallizio, and collective tracts
of the Lettrist International — sourced from
situationist.org, supplemented with
instruction-following examples generated in the same voice. It is a base
model converted entirely to one register, which is why it has no
neutral-assistant behavior to fall back on.
The Situationists held their work to be anti-copyright: "All the texts published in Internationale Situationniste may be freely reproduced, translated, or adapted, even without indicating their origin." This model is offered in that spirit. The weights are released under Apache-2.0 (inherited from the Qwen3-4B-Base base model).
Limitations
- 4B parameters, 4-bit GGUF available. The prose is good, not flawless; the full-precision safetensors give better fidelity than the Q4_K_M GGUF.
- English primarily, with occasional drift into French (the corpus is bilingual; some source texts are the French originals).
- Not factually reliable — see the disclaimer above. It produces SI prose, not information.
- Single-register by design. It cannot be prompted into being a normal assistant. That is the feature.
Citation / corpus
Corpus: the Situationist International archive at situationist.org. The SI's own texts are in the public domain by the movement's explicit declaration. Base model: Qwen/Qwen3-4B-Base.
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Model tree for topherbullock/debord
Base model
Qwen/Qwen3-4B-Base
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="topherbullock/debord", filename="debord-Q4_K_M.gguf", )