Instructions to use harpertoken/tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use harpertoken/tiny with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("harpertoken/tiny") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use harpertoken/tiny with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "harpertoken/tiny"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "harpertoken/tiny" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "harpertoken/tiny", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
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README.md
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> Previously published as `harpertoken/harpertoken-apple-m1-model`. The old identifier still redirects here.
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# tiny
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A four-bit quantised conversion of [`mlx-community/SmolLM-135M-Instruct-4bit`](https://huggingface.co/mlx-community/SmolLM-135M-Instruct-4bit), produced with `mlx-lm` 0.30.4 and intended for Apple Silicon. The architecture is a Llama variant — thirty layers, 576 hidden dimensions, nine attention heads, three key-value heads, 49152-token vocabulary — with group-size 64 four-bit weights, occupying about 76 MB.
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# tiny
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A four-bit quantised conversion of [`mlx-community/SmolLM-135M-Instruct-4bit`](https://huggingface.co/mlx-community/SmolLM-135M-Instruct-4bit), produced with `mlx-lm` 0.30.4 and intended for Apple Silicon. The architecture is a Llama variant — thirty layers, 576 hidden dimensions, nine attention heads, three key-value heads, 49152-token vocabulary — with group-size 64 four-bit weights, occupying about 76 MB.
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