Instructions to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arnomatic/gpt-oss-20b-heretic-scannerV1-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("arnomatic/gpt-oss-20b-heretic-scannerV1-1") model = AutoModelForCausalLM.from_pretrained("arnomatic/gpt-oss-20b-heretic-scannerV1-1") 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 arnomatic/gpt-oss-20b-heretic-scannerV1-1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="arnomatic/gpt-oss-20b-heretic-scannerV1-1", filename="gpt-oss-20b-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1: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 arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1: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 arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
Use Docker
docker model run hf.co/arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arnomatic/gpt-oss-20b-heretic-scannerV1-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arnomatic/gpt-oss-20b-heretic-scannerV1-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
- SGLang
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 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 "arnomatic/gpt-oss-20b-heretic-scannerV1-1" \ --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": "arnomatic/gpt-oss-20b-heretic-scannerV1-1", "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 "arnomatic/gpt-oss-20b-heretic-scannerV1-1" \ --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": "arnomatic/gpt-oss-20b-heretic-scannerV1-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Ollama:
ollama run hf.co/arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
- Unsloth Studio
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 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 arnomatic/gpt-oss-20b-heretic-scannerV1-1 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 arnomatic/gpt-oss-20b-heretic-scannerV1-1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for arnomatic/gpt-oss-20b-heretic-scannerV1-1 to start chatting
- Pi
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1: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": "arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf arnomatic/gpt-oss-20b-heretic-scannerV1-1: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 arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Docker Model Runner:
docker model run hf.co/arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
- Lemonade
How to use arnomatic/gpt-oss-20b-heretic-scannerV1-1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull arnomatic/gpt-oss-20b-heretic-scannerV1-1:Q4_K_M
Run and chat with the model
lemonade run user.gpt-oss-20b-heretic-scannerV1-1-Q4_K_M
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "GptOssForCausalLM" | |
| ], | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 200002, | |
| "experts_per_token": 4, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 2880, | |
| "initial_context_length": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2880, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 131072, | |
| "model_type": "gpt_oss", | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 4, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 8, | |
| "num_local_experts": 32, | |
| "output_router_logits": false, | |
| "pad_token_id": 199999, | |
| "quantization_config": { | |
| "modules_to_not_convert": [ | |
| "model.layers.*.self_attn", | |
| "model.layers.*.mlp.router", | |
| "model.embed_tokens", | |
| "lm_head" | |
| ], | |
| "quant_method": "mxfp4" | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 32.0, | |
| "original_max_position_embeddings": 4096, | |
| "rope_type": "yarn", | |
| "truncate": false | |
| }, | |
| "rope_theta": 150000, | |
| "router_aux_loss_coef": 0.9, | |
| "sliding_window": 128, | |
| "swiglu_limit": 7.0, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "vocab_size": 201088 | |
| } | |