Instructions to use reecdev/Tiny3.5-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reecdev/Tiny3.5-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="reecdev/Tiny3.5-1.5B",
	filename="tiny3.5-1.5b-f16.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 reecdev/Tiny3.5-1.5B 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 reecdev/Tiny3.5-1.5B:F16
# Run inference directly in the terminal:
llama cli -hf reecdev/Tiny3.5-1.5B:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reecdev/Tiny3.5-1.5B:F16
# Run inference directly in the terminal:
llama cli -hf reecdev/Tiny3.5-1.5B:F16

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 reecdev/Tiny3.5-1.5B:F16
# Run inference directly in the terminal:
./llama-cli -hf reecdev/Tiny3.5-1.5B:F16

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 reecdev/Tiny3.5-1.5B:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reecdev/Tiny3.5-1.5B:F16

Use Docker

docker model run hf.co/reecdev/Tiny3.5-1.5B:F16

LM Studio
Jan
Ollama
How to use reecdev/Tiny3.5-1.5B with Ollama:
```
ollama run hf.co/reecdev/Tiny3.5-1.5B:F16
```

Unsloth Studio

How to use reecdev/Tiny3.5-1.5B 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 reecdev/Tiny3.5-1.5B 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 reecdev/Tiny3.5-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reecdev/Tiny3.5-1.5B to start chatting

How to use reecdev/Tiny3.5-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reecdev/Tiny3.5-1.5B:F16

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": "reecdev/Tiny3.5-1.5B:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use reecdev/Tiny3.5-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reecdev/Tiny3.5-1.5B:F16

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 reecdev/Tiny3.5-1.5B:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use reecdev/Tiny3.5-1.5B with Docker Model Runner:
```
docker model run hf.co/reecdev/Tiny3.5-1.5B:F16
```

Lemonade

How to use reecdev/Tiny3.5-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reecdev/Tiny3.5-1.5B:F16

Run and chat with the model

lemonade run user.Tiny3.5-1.5B-F16

List all available models

lemonade list

Tiny3.5

An attempt to compress Qwen3.5 into 500M and 1.5B parameters.

What is this?

Tiny3.5 is my community effort to create tiny and more efficient versions of Qwen3.5. The strengths of Tiny3.5 include very low inference latency, minimal overthinking, and being able to run on much weaker hardware. However, it's important to realize that Tiny3.5 is sub-2B parameters. Don't expect a 99% score on every single benchmark.

How is this better than Qwen3.5?

Tiny3.5 uses many techniques to produce better efficiency than Qwen3.5 in many scenarios. We use multi-shot distillation to filter out pointless reasoning loops and improve the overall quality of responses.

Can I create my own model using the Tiny3.5 dataset?

Absolutely! Our distillation dataset is open-source, and the code used to create it alongside a copy of the dataset is available on our GitHub: https://github.com/reecdev/tiny3.5

Downloads last month: 5

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

16-bit

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Model tree for reecdev/Tiny3.5-1.5B

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Model tree for reecdev/Tiny3.5-1.5B

Dataset used to train reecdev/Tiny3.5-1.5B