Instructions to use QuantFactory/TinyAgent-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/TinyAgent-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/TinyAgent-7B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/TinyAgent-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/TinyAgent-7B-GGUF",
	filename="TinyAgent-7B.Q2_K.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 QuantFactory/TinyAgent-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/TinyAgent-7B-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 QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/TinyAgent-7B-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 QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/TinyAgent-7B-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 QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/TinyAgent-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/TinyAgent-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/TinyAgent-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/TinyAgent-7B-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 QuantFactory/TinyAgent-7B-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 QuantFactory/TinyAgent-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/TinyAgent-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/TinyAgent-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/TinyAgent-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/TinyAgent-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/TinyAgent-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyAgent-7B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/TinyAgent-7B-GGUF

This is quantized version of squeeze-ai-lab/TinyAgent-7B created using llama.cpp

Original Model Card

TinyAgent: Function Calling at the Edge

Get the desktop app‎ ‎ | Read the blog post

TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with LLMCompiler. As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings.

Model Developers: Squeeze AI Lab at University of California, Berkeley.

Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B

License: MIT

Demo

How to Use

Please see our Github for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface.

Training Details

Dataset: We curated a dataset of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our blog post for more details on our dataset.

Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart

Model	Success Rate
GPT-3.5-turbo	65.04%
GPT-4-turbo	79.08%
TinyLLama-1.1B-32K-Instruct	12.71%
WizardLM-2-7b	41.25%
TinyAgent-1.1B + ToolRAG / [hf] [gguf]	80.06%
TinyAgent-7B + ToolRAG / [hf] [gguf]	84.95%

Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our blog post for more details on our fine-tuning procedure.

🛠️ ToolRAG

When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our blog post and our ToolRAG model for more details.

QuantFactory
/

TinyAgent-7B-GGUF