Instructions to use reecdev/Tiny3.5-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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 Desktop
- Pi
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 @earendil-works/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
- 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
- Hermes Agent
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
- OpenClaw
How to use reecdev/Tiny3.5-1.5B with OpenClaw:
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 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 "reecdev/Tiny3.5-1.5B:F16" \ --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"
|
Download README.md from reecdev/Tiny3.5-1.5B: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/reecdev/Tiny3.5-1.5B/resolve/main/README.md
- Command line
-
hf download hf://reecdev/Tiny3.5-1.5B/README.md
-
curl -L -o README.md https://huggingface.co/reecdev/Tiny3.5-1.5B/resolve/main/README.md
1.02 kB
| license: apache-2.0 | |
| datasets: | |
| - reecdev/Tiny3.5-Multi-Distil | |
| language: | |
| - en | |
| base_model: | |
| - Qwen/Qwen2.5-1.5B | |
| # 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 |