- 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")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("3sk4p3/bastion", dtype="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 litert-lm
# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
litert-lm run \
--from-huggingface-repo=3sk4p3/bastion \
model.litertlm \
--prompt="Write me a poem"
- llama-cpp-python
How to use 3sk4p3/bastion with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="3sk4p3/bastion",
filename="bastion-mmproj.BF16.gguf",
)
llm.create_chat_completion(
messages = "\"I like you. I love you\""
)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use 3sk4p3/bastion with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -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-server -hf 3sk4p3/bastion:BF16
# Run inference directly in the terminal:
llama-cli -hf 3sk4p3/bastion:BF16
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 3sk4p3/bastion:BF16
# Run inference directly in the terminal:
./llama-cli -hf 3sk4p3/bastion:BF16
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 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
- LM Studio
- Jan
- Ollama
How to use 3sk4p3/bastion with Ollama:
ollama run hf.co/3sk4p3/bastion:BF16
- Unsloth Studio
How to use 3sk4p3/bastion 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 3sk4p3/bastion 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 3sk4p3/bastion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 3sk4p3/bastion to start chatting
- 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-server -hf 3sk4p3/bastion:BF16
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": "3sk4p3/bastion:BF16"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
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-server -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
- 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
