Instructions to use alpha-ai/AlphaAI-Chatty-INT1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpha-ai/AlphaAI-Chatty-INT1 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alpha-ai/AlphaAI-Chatty-INT1", dtype="auto")

llama-cpp-python

How to use alpha-ai/AlphaAI-Chatty-INT1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alpha-ai/AlphaAI-Chatty-INT1",
	filename="AlphaAI-Chatty-INT1.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-INT1 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 alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alpha-ai/AlphaAI-Chatty-INT1: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-INT1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alpha-ai/AlphaAI-Chatty-INT1: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-INT1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M

Use Docker

docker model run hf.co/alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M

LM Studio
Jan
Ollama
How to use alpha-ai/AlphaAI-Chatty-INT1 with Ollama:
```
ollama run hf.co/alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M
```

Unsloth Studio

How to use alpha-ai/AlphaAI-Chatty-INT1 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-INT1 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-INT1 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-INT1 to start chatting

How to use alpha-ai/AlphaAI-Chatty-INT1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/AlphaAI-Chatty-INT1: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-INT1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use alpha-ai/AlphaAI-Chatty-INT1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/AlphaAI-Chatty-INT1: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-INT1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use alpha-ai/AlphaAI-Chatty-INT1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use alpha-ai/AlphaAI-Chatty-INT1 with Docker Model Runner:
```
docker model run hf.co/alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M
```

Lemonade

How to use alpha-ai/AlphaAI-Chatty-INT1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alpha-ai/AlphaAI-Chatty-INT1:Q4_K_M

Run and chat with the model

lemonade run user.AlphaAI-Chatty-INT1-Q4_K_M

List all available models

lemonade list

Website - https://www.alphaai.biz

Uploaded model

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

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

AlphaAI-Chatty-INT1

Overview AlphaAI-Chatty-INT1 is a fine-tuned LLaMA 3B Small model optimized for chatty and engaging conversations. This model has been trained on a proprietary conversational dataset, making it well-suited for local deployments that require a natural, interactive dialogue experience.

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

Model Details

Base Model: LLaMA 3B Small
Fine-tuned By: Alpha AI
Training Framework: Unsloth

Quantization Levels Available:

q4_k_m
q5_k_m
q8_0
16-bit (full precision) https://huggingface.co/alphaaico/AlphaAI-Chatty-INT1-16bit

Format: GGUF (Optimized for local deployments)

Use Cases:

Conversational AI – Ideal for chatbots, virtual assistants, and customer support.
Local AI Deployments – Runs efficiently on local machines without requiring cloud-based inference.
Research & Experimentation – Suitable for studying conversational AI and fine-tuning on domain-specific datasets.

Model Performance The model has been optimized for chat-style interactions, ensuring:

Engaging and context-aware responses
Efficient performance on consumer hardware
Balanced coherence and creativity in conversations

Limitations & Biases This model, like any AI system, may have biases from the training data. It is recommended to use it responsibly and fine-tune further if needed for specific applications.

License This model is released under a permissible license. Please check the Hugging Face repository for more details.

Acknowledgments Special thanks to the Unsloth team for providing an optimized training pipeline for LLaMA models.

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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Collection including alpha-ai/AlphaAI-Chatty-INT1

Finetunes | SLMs and LLMs

Collection

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