Instructions to use im-lemon/tinychatbot-1b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use im-lemon/tinychatbot-1b-v1.0 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 im-lemon/tinychatbot-1b-v1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf im-lemon/tinychatbot-1b-v1.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf im-lemon/tinychatbot-1b-v1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf im-lemon/tinychatbot-1b-v1.0: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 im-lemon/tinychatbot-1b-v1.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf im-lemon/tinychatbot-1b-v1.0: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 im-lemon/tinychatbot-1b-v1.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf im-lemon/tinychatbot-1b-v1.0:Q4_K_M

Use Docker

docker model run hf.co/im-lemon/tinychatbot-1b-v1.0:Q4_K_M

LM Studio
Jan
Ollama
How to use im-lemon/tinychatbot-1b-v1.0 with Ollama:
```
ollama run hf.co/im-lemon/tinychatbot-1b-v1.0:Q4_K_M
```

Unsloth Studio

How to use im-lemon/tinychatbot-1b-v1.0 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 im-lemon/tinychatbot-1b-v1.0 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 im-lemon/tinychatbot-1b-v1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for im-lemon/tinychatbot-1b-v1.0 to start chatting

How to use im-lemon/tinychatbot-1b-v1.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf im-lemon/tinychatbot-1b-v1.0: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": "im-lemon/tinychatbot-1b-v1.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use im-lemon/tinychatbot-1b-v1.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf im-lemon/tinychatbot-1b-v1.0: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 "im-lemon/tinychatbot-1b-v1.0: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 im-lemon/tinychatbot-1b-v1.0 with Docker Model Runner:
```
docker model run hf.co/im-lemon/tinychatbot-1b-v1.0:Q4_K_M
```

Lemonade

How to use im-lemon/tinychatbot-1b-v1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull im-lemon/tinychatbot-1b-v1.0:Q4_K_M

Run and chat with the model

lemonade run user.tinychatbot-1b-v1.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use im-lemon/tinychatbot-1b-v1.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf im-lemon/tinychatbot-1b-v1.0: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 im-lemon/tinychatbot-1b-v1.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

Tinychatbot-1b-v1.0 (Aditional Info)

tinychatbot-1b-v1.0 is a QLoRA finetune of Llama3.2-1b-Instruct, it was finetuned on the dolly-databricks. It is a proof of concept, as the final tinychatbot will be trained on dolly databricks and a custom personality dataset!

It was trained on Google Colab, using unsloth Studio, with QLoRA. It is lightweight (808MB), and has a 128k token context-length.

It has 1 Billion parameters. Other models will be coming soon, such as:

Tinychatbot-v1.1
Tinychatbot-v.1.2

Other types of models may include:

Vision (if possible)
Image models

Downloads last month: 44

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

im-lemon
/

tinychatbot-1b-v1.0

Tinychatbot-1b-v1.0 (Aditional Info)

Space using im-lemon/tinychatbot-1b-v1.0 1