Instructions to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF", filename="Ornstein3.6-35B-A3B-RYS-F16.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/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-GGUF:F16 # Run inference directly in the terminal: llama cli -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16 # Run inference directly in the terminal: llama cli -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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/Ornstein3.6-35B-A3B-RYS-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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/Ornstein3.6-35B-A3B-RYS-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Use Docker
docker model run hf.co/GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GestaltLabs/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Ollama
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with Ollama:
ollama run hf.co/GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Unsloth Studio
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-GGUF to start chatting
- Pi
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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/Ornstein3.6-35B-A3B-RYS-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-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/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with Docker Model Runner:
docker model run hf.co/GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Lemonade
How to use GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GestaltLabs/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Run and chat with the model
lemonade run user.Ornstein3.6-35B-A3B-RYS-GGUF-F16
List all available models
lemonade list
Ornstein3.6-35B-A3B-RYS-Q6_K.gguf throws llama-cli error: "missing tensor 'blk.11.attn_q.weight'"
Attempting to run with llama-cli:
create_tensor: loading tensor blk.10.ffn_up_exps.weight
create_tensor: loading tensor blk.10.ffn_gate_inp_shexp.weight
create_tensor: loading tensor blk.10.ffn_gate_shexp.weight
create_tensor: loading tensor blk.10.ffn_up_shexp.weight
create_tensor: loading tensor blk.10.ffn_down_shexp.weight
create_tensor: loading tensor blk.11.attn_norm.weight
create_tensor: loading tensor blk.11.post_attention_norm.weight
llama_model_load: error loading model: missing tensor 'blk.11.attn_q.weight'
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model './Ornstein3.6-35B-A3B-RYS-Q6_K.gguf'
srv load_model: failed to load model, './Ornstein3.6-35B-A3B-RYS-Q6_K.gguf'
Failed to load the model
~/code/llama.cpp/gguf-py/gguf/scripts/gguf_dump.py Ornstein3.6-35B-A3B-RYS-Q6_K.gguf | grep "attn_q.weight"
yields:
INFO:gguf-dump:* Loading: Ornstein3.6-35B-A3B-RYS-Q6_K.gguf
65: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.3.attn_q.weight
138: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.7.attn_q.weight
230: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.12.attn_q.weight
303: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.16.attn_q.weight
376: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.20.attn_q.weight
449: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.24.attn_q.weight
522: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.28.attn_q.weight
595: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.32.attn_q.weight
668: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.36.attn_q.weight
741: 16777216 | 2048, 8192, 1, 1 | Q6_K | blk.40.attn_q.weight
I saw that other RYS models use a forked llama.cpp. Using that,
'''~/code/DJLougen/llama.cpp-rys-qwen35/build/bin/llama-cli -m ./Ornstein3.6-35B-A3B-RYS-Q6_K.gguf --min-p 0.01 --temp 1.0 --top-p 0.95 --top-k 20 -c 65536 -ngl 18```
still gives
Loading model... |llama_model_load: error loading model: missing tensor 'blk.11.attn_q.weight'
llama_model_load_from_file_impl: failed to load model
and the forked code gguf_dump.py output is unchanged.