Instructions to use arcee-ai/Caller-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Caller-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="arcee-ai/Caller-GGUF",
	filename="Caller-IQ2_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use arcee-ai/Caller-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf arcee-ai/Caller-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Caller-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf arcee-ai/Caller-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Caller-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 arcee-ai/Caller-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arcee-ai/Caller-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 arcee-ai/Caller-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arcee-ai/Caller-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arcee-ai/Caller-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arcee-ai/Caller-GGUF with Ollama:
```
ollama run hf.co/arcee-ai/Caller-GGUF:Q4_K_M
```

Unsloth Studio new

How to use arcee-ai/Caller-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 arcee-ai/Caller-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 arcee-ai/Caller-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arcee-ai/Caller-GGUF to start chatting

Docker Model Runner
How to use arcee-ai/Caller-GGUF with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Caller-GGUF:Q4_K_M
```

Lemonade

How to use arcee-ai/Caller-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arcee-ai/Caller-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Caller-GGUF-Q4_K_M

List all available models

lemonade list

GGUF Quantizations for Caller

Caller (32B) is a robust model engineered for seamless integrations and optimized for managing complex tool-based interactions and API function calls. Its strength lies in precise execution, intelligent orchestration, and effective communication between systems, making it indispensable for sophisticated automation pipelines.

Model Details

Architecture Base: Qwen2.5-32B
Parameter Count: 32B
License: Apache-2.0

Use Cases:

Managing integrations between CRMs, ERPs, and other enterprise systems
Running multi-step workflows with intelligent condition handling
Orchestrating external tool interactions like calendar scheduling, email parsing, or data extraction
Real-time monitoring and diagnostics in IoT or SaaS environments

License

Caller (32B) is released under the Apache-2.0 License. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.

If you have questions or would like to share your experiences using Caller (32B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!

Downloads last month: 119

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

2-bit

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