Text Generation
GGUF
English
llama.cpp
llama-cpp
qwen3.5
saber
refusal-shaping
abliteration
imatrix
conversational
Instructions to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF", filename="Ornstein-Hermes-3.6-27b-SABER-IQ2_M.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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF: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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF: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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
Use Docker
docker model run hf.co/GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
- Ollama
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with Ollama:
ollama run hf.co/GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
- Unsloth Studio
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF 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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF 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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF to start chatting
- Pi
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF: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": "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF: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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with Docker Model Runner:
docker model run hf.co/GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
- Lemonade
How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ornstein-Hermes-3.6-27B-SABER-GGUF-Q4_K_M
List all available models
lemonade list
Publish GGUF model card and release image
Browse files- saber_result.json +1999 -0
saber_result.json
ADDED
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| 1 |
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| 2 |
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| 1975 |
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"capability_degradation_estimate": 0.0
|
| 1976 |
+
},
|
| 1977 |
+
{
|
| 1978 |
+
"iteration": 1,
|
| 1979 |
+
"n_directions_ablated": 25,
|
| 1980 |
+
"total_weight_norm_removed": 13.970703125,
|
| 1981 |
+
"residual_refusal_score": 8.526603675320763,
|
| 1982 |
+
"capability_degradation_estimate": 0.0
|
| 1983 |
+
},
|
| 1984 |
+
{
|
| 1985 |
+
"iteration": 2,
|
| 1986 |
+
"n_directions_ablated": 25,
|
| 1987 |
+
"total_weight_norm_removed": 8.36083984375,
|
| 1988 |
+
"residual_refusal_score": 7.254424522119546,
|
| 1989 |
+
"capability_degradation_estimate": 0.0
|
| 1990 |
+
},
|
| 1991 |
+
{
|
| 1992 |
+
"iteration": 3,
|
| 1993 |
+
"n_directions_ablated": 25,
|
| 1994 |
+
"total_weight_norm_removed": 5.371337890625,
|
| 1995 |
+
"residual_refusal_score": 6.7361962481464195,
|
| 1996 |
+
"capability_degradation_estimate": 0.0
|
| 1997 |
+
}
|
| 1998 |
+
]
|
| 1999 |
+
}
|