Instructions to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF",
	filename="model-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF 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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF: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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF: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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with Ollama:
```
ollama run hf.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
```

Unsloth Studio

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF to start chatting

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF: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": "ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

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 ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with Docker Model Runner:
```
docker model run hf.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M
```

Lemonade

How to use ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF-Q4_K_M

List all available models

lemonade list

Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF

GGUF quantized versions of ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible tools.

Available Quantizations

File	Quantization	Quality	Use Case
`llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf`	Q4_K_M	Good	Recommended - Best balance of quality and size
`llama-3.2-1b-instruct_function_calling_xlam-q5_k_m.gguf`	Q5_K_M	Better	Higher quality, moderate size increase
`llama-3.2-1b-instruct_function_calling_xlam-q8_0.gguf`	Q8_0	Best	Highest quality quantization

Download Specific Quantization

Using huggingface-cli

# Download Q4_K_M (recommended)
huggingface-cli download ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf --local-dir ./models

# Download Q5_K_M (higher quality)
huggingface-cli download ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF llama-3.2-1b-instruct_function_calling_xlam-q5_k_m.gguf --local-dir ./models

# Download Q8_0 (best quality)
huggingface-cli download ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF llama-3.2-1b-instruct_function_calling_xlam-q8_0.gguf --local-dir ./models

# Download all quantizations
huggingface-cli download ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF --local-dir ./models

Using wget

# Q4_K_M
wget https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF/resolve/main/llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf

# Q5_K_M
wget https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF/resolve/main/llama-3.2-1b-instruct_function_calling_xlam-q5_k_m.gguf

# Q8_0
wget https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF/resolve/main/llama-3.2-1b-instruct_function_calling_xlam-q8_0.gguf

Usage

Ollama

# Pull specific quantization
ollama pull hf.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF:Q4_K_M

# Or create from local file
cat > Modelfile << EOF
FROM ./llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf
EOF

ollama create llama-3.2-1b-instruct_function_calling_xlam -f Modelfile
ollama run llama-3.2-1b-instruct_function_calling_xlam

llama.cpp

# Run with llama-cli
./llama-cli -m llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf -p "Your prompt here" -n 256

# Run as server
./llama-server -m llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf --host 0.0.0.0 --port 8080

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="llama-3.2-1b-instruct_function_calling_xlam-q4_k_m.gguf",
    n_ctx=2048,
    n_gpu_layers=-1  # Use all GPU layers
)

output = llm(
    "What is machine learning?",
    max_tokens=256,
    temperature=0.7,
)
print(output['choices'][0]['text'])

LM Studio

Download the desired GGUF file from this repository
Open LM Studio and navigate to the Models tab
Click "Add Model" and select the downloaded GGUF file
Load the model and start chatting

GPT4All

Download the Q4_K_M GGUF file
Open GPT4All and go to Settings > Models
Add the GGUF file path
Select the model and start using

Original Model

This is a quantized version of ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM. See the original model card for:

Training details and methodology
Dataset information
Performance metrics
Full usage examples with Transformers

Conversion Details

Property	Value
Source Model	ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM
Conversion Date	2026-04-09
Quantizations	Q4_K_M, Q5_K_M, Q8_0
Converter	llama.cpp

License

Same license as the original model. See ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM for details.

Converted using the Slurm Model Trainer skill

Citation

If you use this model in your research or applications, please cite:

@misc{azarkhalili2026_llama_3_2_1b_instruct_function_calling_xlam_gguf,
    author = {Azarkhalili, Behrooz},
    title = {Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF}
}

To generate a citable DOI, click "Cite this model" on the model page.

Training Method

Trained using SFT (Supervised Fine-Tuning) on Salesforce xLAM Function Calling 60K.

Base (merged) model: Llama-3.2-1B-Instruct_Function_Calling_xLAM

Acknowledgments

Hugging Face TRL Team for the training library
llama.cpp for the GGUF quantization format
Compute Canada / DRAC for HPC resources
Base model developers for making their weights openly available

Downloads last month: 37

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM-GGUF

Base model

meta-llama/Llama-3.2-1B-Instruct

Adapter

ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM

Quantized

(1)

this model