Instructions to use Ilides/cortex-0.3-0.02b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Ilides/cortex-0.3-0.02b-mlx 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("Ilides/cortex-0.3-0.02b-mlx") 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 Ilides/cortex-0.3-0.02b-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Ilides/cortex-0.3-0.02b-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Ilides/cortex-0.3-0.02b-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ilides/cortex-0.3-0.02b-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
cortex-0.3-0.02b 路 MLX (Apple Silicon)
Versi贸n MLX (Metal) de Ilides/cortex-0.3-0.02b, el GPT conversacional de 20.6 M par谩metros entrenado desde cero en NumPy puro. Ejecuta nativamente en Apple Silicon sin PyTorch.
Requisitos
pip install mlx safetensors transformers regex
Uso
# bater铆a de prompts de prueba (por defecto)
python generate_mlx.py
# una pregunta
python generate_mlx.py "驴Qu茅 eres?" --max-new 128
# chat interactivo (escribe "salir" para terminar)
python generate_mlx.py --interactive --temperature 0.8
Bundle autocontenido: weights.safetensors (45 tensores) + model.py (arquitectura) + generate_mlx.py (CLI) + tokenizer. No depende de mlx-lm ni de c贸digo externo del Hub.
Verificaci贸n
Paridad de logits MLX vs NumPy (implementaci贸n de referencia): max|螖| = 3.65e-05 (< 1e-4). Tokens EOS efectivos: {0, 6}.
Arquitectura
cortex: embeddings de posici贸n aprendidos (sin RoPE), RMSNorm + atenci贸n multi-cabeza + SwiGLU, embeddings atados (tie_word_embeddings). Contexto 512, 6 capas, d_model 192, vocab 16384 (BPE byte-level).
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