- Libraries
- Transformers
How to use tensorblock/SummLlama3.2-3B-GGUF with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("summarization", model="tensorblock/SummLlama3.2-3B-GGUF")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/SummLlama3.2-3B-GGUF", dtype="auto") - llama-cpp-python
How to use tensorblock/SummLlama3.2-3B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="tensorblock/SummLlama3.2-3B-GGUF",
filename="SummLlama3.2-3B-Q2_K.gguf",
)
llm.create_chat_completion(
messages = "\"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\""
)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tensorblock/SummLlama3.2-3B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
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 tensorblock/SummLlama3.2-3B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
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 tensorblock/SummLlama3.2-3B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/SummLlama3.2-3B-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/SummLlama3.2-3B-GGUF with Ollama:
ollama run hf.co/tensorblock/SummLlama3.2-3B-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/SummLlama3.2-3B-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 tensorblock/SummLlama3.2-3B-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 tensorblock/SummLlama3.2-3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/SummLlama3.2-3B-GGUF to start chatting
- Pi new
How to use tensorblock/SummLlama3.2-3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
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": "tensorblock/SummLlama3.2-3B-GGUF:Q2_K"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent new
How to use tensorblock/SummLlama3.2-3B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/SummLlama3.2-3B-GGUF:Q2_K
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 tensorblock/SummLlama3.2-3B-GGUF:Q2_K
Run Hermes
hermes
- Docker Model Runner
How to use tensorblock/SummLlama3.2-3B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/SummLlama3.2-3B-GGUF:Q2_K
- Lemonade
How to use tensorblock/SummLlama3.2-3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/SummLlama3.2-3B-GGUF:Q2_K
Run and chat with the model
lemonade run user.SummLlama3.2-3B-GGUF-Q2_K
List all available models
lemonade list
