Libraries Transformers How to use 3sk4p3/bastion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="3sk4p3/bastion") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("3sk4p3/bastion", device_map="auto") LiteRT How to use 3sk4p3/bastion with LiteRT:
# No code snippets available yet for this library.
# To use this model, check the repository files and the library's documentation.
# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js LiteRT-LM How to use 3sk4p3/bastion with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm
# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install -U litert-lm
# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
# A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run
# (or pass its name right after the repo id).
litert-lm run \
--from-huggingface-repo=3sk4p3/bastion \
--prompt="Write me a poem" Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use 3sk4p3/bastion 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 3sk4p3/bastion:BF16
# Run inference directly in the terminal:
llama cli -hf 3sk4p3/bastion:BF16 Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 3sk4p3/bastion:BF16
# Run inference directly in the terminal:
llama cli -hf 3sk4p3/bastion:BF16 Build from source code git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf 3sk4p3/bastion:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf 3sk4p3/bastion:BF16 Use Docker docker model run hf.co/3sk4p3/bastion:BF16 Llama new LM Studio Jan Ollama How to use 3sk4p3/bastion with Ollama:
ollama run hf.co/3sk4p3/bastion:BF16 Unsloth Desktop Pi How to use 3sk4p3/bastion with Pi:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 3sk4p3/bastion:BF16 Configure the model in Pi # Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "3sk4p3/bastion:BF16"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi Docker Model Runner How to use 3sk4p3/bastion with Docker Model Runner:
docker model run hf.co/3sk4p3/bastion:BF16 Lemonade How to use 3sk4p3/bastion with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull 3sk4p3/bastion:BF16 Run and chat with the model lemonade run user.bastion-BF16 List all available models lemonade list Hermes Agent How to use 3sk4p3/bastion with Hermes Agent:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 3sk4p3/bastion:BF16 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 3sk4p3/bastion:BF16 Run Hermes hermes Atomic Chat OpenClaw How to use 3sk4p3/bastion with OpenClaw:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 3sk4p3/bastion:BF16 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 "3sk4p3/bastion:BF16" \
--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" 
