Instructions to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alpha-ai/AlphaAI-Chatty-INT2-GGUF", dtype="auto")

llama-cpp-python

How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alpha-ai/AlphaAI-Chatty-INT2-GGUF",
	filename="AlphaAI-Chatty-INT2.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 alpha-ai/AlphaAI-Chatty-INT2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with Ollama:
```
ollama run hf.co/alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
```

Unsloth Studio

How to use alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alpha-ai/AlphaAI-Chatty-INT2-GGUF to start chatting

How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf alpha-ai/AlphaAI-Chatty-INT2-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": "alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-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 alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with Docker Model Runner:
```
docker model run hf.co/alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M
```

Lemonade

How to use alpha-ai/AlphaAI-Chatty-INT2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alpha-ai/AlphaAI-Chatty-INT2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AlphaAI-Chatty-INT2-GGUF-Q4_K_M

List all available models

lemonade list

Website - https://www.alphaai.biz

Uploaded Model

Developed by: Alpha AI
License: apache-2.0
Finetuned from model: meta-llama/Llama-3.2-3B-Instruct

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

AlphaAI-Chatty-INT2

Overview

AlphaAI-Chatty-INT2 is a fine-tuned meta-llama/Llama-3.2-3B-Instruct model optimized for empathic, chatty, and engaging conversations. Building on the foundations of our INT1 release, the INT2 version includes enhanced conversational capabilities that make it more context-aware, responsive, and personable. Trained on an improved proprietary conversational dataset, this model is particularly suitable for local deployments requiring a natural, interactive, and empathetic dialogue experience.

The model is available in GGUF format and has been quantized to different levels to support various hardware configurations.

This model is an upgrade to AlphaAI-Chatty-INT1. You can find and use the previous models from here.

Model Details

Base Model: meta-llama/Llama-3.2-3B-Instruct
Fine-tuned By: Alpha AI
Training Framework: Unsloth

Quantization Levels Available

q4_k_m
q5_k_m
q8_0
16-bit (full precision) - Link

(Note: The INT1 16-bit link is referenced (https://huggingface.co/alphaaico/AlphaAI-Chatty-INT1)

Format: GGUF (Optimized for local deployments)

Use Cases

Conversational AI – Ideal for chatbots, virtual assistants, and customer support where empathetic and engaging interaction is crucial.
Local AI Deployments – Runs efficiently on local machines, negating the need for cloud-based inference.
Research & Experimentation – Suitable for studying advanced conversational AI techniques and fine-tuning on specialized or proprietary datasets.

Model Performance

AlphaAI-Chatty-INT2 has been further optimized to deliver:

Empathic and Context-Aware Responses – Improved understanding of user inputs with a focus on empathetic replies.
High Efficiency on Consumer Hardware – Maintains quick inference speeds even with more advanced conversation modeling.
Balanced Coherence and Creativity – Strikes an ideal balance for real-world dialogue applications, allowing for both coherent answers and creative flair.

Limitations & Biases

Like any AI system, this model may exhibit biases stemming from its training data. Users should employ it responsibly and consider additional fine-tuning if needed for sensitive or specialized applications.

License

Released under the Apache-2.0 license. For full details, please consult the license file in the Hugging Face repository.

Acknowledgments

Special thanks to the Unsloth team for their optimized training pipeline for LLaMA models. Additional appreciation goes to Hugging Face’s TRL library for enabling accelerated and efficient fine-tuning workflows.

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for alpha-ai/AlphaAI-Chatty-INT2-GGUF

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Collection including alpha-ai/AlphaAI-Chatty-INT2-GGUF

Finetunes | SLMs and LLMs

Collection

Various variants of LLMs finetuned using proprietary data. • 27 items • Updated Jan 19 • 4