Text Generation
Transformers
Safetensors
GGUF
English
French
qwen3
situationist
guy-debord
style-transfer
creative-writing
persona
conversational
text-generation-inference
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
| FROM ./debord-Q4_K_M.gguf | |
| TEMPLATE """{{ if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}{{ if .Prompt }}<|im_start|>user | |
| {{ .Prompt }}<|im_end|> | |
| {{ end }}<|im_start|>assistant | |
| <think> | |
| </think> | |
| {{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""" | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|im_start|>" | |
| PARAMETER stop "<|endoftext|>" | |
| PARAMETER temperature 0.9 | |
| PARAMETER top_p 0.95 | |
| PARAMETER repeat_penalty 1.15 | |
| PARAMETER min_p 0.05 | |
| PARAMETER num_predict 400 | |
| 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.""" | |