Instructions to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dil99x/SummLlama3.2-3B-Q2_K-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="dil99x/SummLlama3.2-3B-Q2_K-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dil99x/SummLlama3.2-3B-Q2_K-GGUF", dtype="auto")

llama-cpp-python

How to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with Ollama:
```
ollama run hf.co/dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
```

Unsloth Studio new

How to use dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dil99x/SummLlama3.2-3B-Q2_K-GGUF to start chatting

Pi new

How to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dil99x/SummLlama3.2-3B-Q2_K-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": "dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-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 dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K
```

Lemonade

How to use dil99x/SummLlama3.2-3B-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dil99x/SummLlama3.2-3B-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.SummLlama3.2-3B-Q2_K-GGUF-Q2_K

List all available models

lemonade list

dil99x/SummLlama3.2-3B-Q2_K-GGUF

This model was converted to GGUF format from DISLab/SummLlama3.2-3B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo dil99x/SummLlama3.2-3B-Q2_K-GGUF --hf-file summllama3.2-3b-q2_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo dil99x/SummLlama3.2-3B-Q2_K-GGUF --hf-file summllama3.2-3b-q2_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo dil99x/SummLlama3.2-3B-Q2_K-GGUF --hf-file summllama3.2-3b-q2_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo dil99x/SummLlama3.2-3B-Q2_K-GGUF --hf-file summllama3.2-3b-q2_k.gguf -c 2048

Downloads last month: 3

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

Model tree for dil99x/SummLlama3.2-3B-Q2_K-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

DISLab/SummLlama3.2-3B

Quantized

(7)

this model