Instructions to use Combatti/llama3.2-3B-FunctionCalling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Combatti/llama3.2-3B-FunctionCalling with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Combatti/llama3.2-3B-FunctionCalling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Combatti/llama3.2-3B-FunctionCalling", dtype="auto")

llama-cpp-python

How to use Combatti/llama3.2-3B-FunctionCalling with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Combatti/llama3.2-3B-FunctionCalling",
	filename="unsloth.Q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Combatti/llama3.2-3B-FunctionCalling with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

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 Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

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 Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

Use Docker

docker model run hf.co/Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

LM Studio
Jan

vLLM

How to use Combatti/llama3.2-3B-FunctionCalling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Combatti/llama3.2-3B-FunctionCalling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Combatti/llama3.2-3B-FunctionCalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

SGLang

How to use Combatti/llama3.2-3B-FunctionCalling with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Combatti/llama3.2-3B-FunctionCalling" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Combatti/llama3.2-3B-FunctionCalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Combatti/llama3.2-3B-FunctionCalling" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Combatti/llama3.2-3B-FunctionCalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Combatti/llama3.2-3B-FunctionCalling with Ollama:
```
ollama run hf.co/Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
```

Unsloth Studio new

How to use Combatti/llama3.2-3B-FunctionCalling 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 Combatti/llama3.2-3B-FunctionCalling 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 Combatti/llama3.2-3B-FunctionCalling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Combatti/llama3.2-3B-FunctionCalling to start chatting

Pi new

How to use Combatti/llama3.2-3B-FunctionCalling with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

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": "llama3.2-3B-FunctionCalling"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Combatti/llama3.2-3B-FunctionCalling with Docker Model Runner:
```
docker model run hf.co/Combatti/llama3.2-3B-FunctionCalling:Q4_K_M
```

Lemonade

How to use Combatti/llama3.2-3B-FunctionCalling with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Combatti/llama3.2-3B-FunctionCalling:Q4_K_M

Run and chat with the model

lemonade run user.llama3.2-3B-FunctionCalling-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Uploaded model

Developed by: Combatti
License: apache-2.0
Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 16

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit