Text Classification
MLX
Safetensors
English
qwen2
sifta
alice
classifier
intent-detection
apple-silicon
lora
fine-tuned
organism
stigmergy
4-bit precision
Instructions to use georgeanton/alice-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use georgeanton/alice-classifier-v2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir alice-classifier-v2 georgeanton/alice-classifier-v2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
feat: Alice Classifier v2 β SIFTA C1 intent layer (LoRA fused, MLX, Qwen2.5-1.5B). For the Swarm.
93685ae verified | language: | |
| - en | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen2.5-1.5B | |
| tags: | |
| - sifta | |
| - alice | |
| - classifier | |
| - intent-detection | |
| - mlx | |
| - apple-silicon | |
| - lora | |
| - fine-tuned | |
| - organism | |
| - stigmergy | |
| library_name: mlx | |
| pipeline_tag: text-classification | |
| # Alice Classifier v2 β SIFTA Intent Detection (C1 Layer) | |
| **Alice's fast intent classifier**, the C1 layer in SIFTA's five-layer decision pipeline. | |
| Part of the [SIFTA Predator OS v7.0](https://github.com/antonpictures/ANTON-SIFTA). | |
| ## Model Details | |
| | Property | Value | | |
| |---|---| | |
| | **Base Model** | Qwen2.5-1.5B-4bit (via mlx-community) | | |
| | **Fine-tune Method** | LoRA (rank 8) fused into base weights | | |
| | **Format** | MLX SafeTensors (Apple Silicon optimized) | | |
| | **Training Hardware** | Mac Studio M2 Ultra (M5 node) | | |
| | **Author** | Ioan George Anton (Architect) | | |
| | **Purpose** | Fast intent classification before expensive C0 cortex fires | | |
| ## Architecture Role | |
| This model is the **C1 Classifier** β the second layer in SIFTA's five-layer decision pipeline: | |
| 1. **Reflex Arc** β instant safety responses | |
| 2. **C1 Classifier (THIS MODEL)** β fast intent detection (~1.5B, sub-second) | |
| 3. **Basal Ganglia** β action selection | |
| 4. **Corpus Callosum** β cross-modal integration | |
| 5. **C0 Cortex** β full reasoning ([alice-cortex-v1](https://huggingface.co/georgeanton/alice-cortex-v1)) | |
| **Why two models?** The C1 classifier handles ~80% of incoming intents at 1/3 the compute cost. The expensive C0 cortex only fires when the classifier can't resolve the intent. This is biological: your brainstem handles reflexes before your prefrontal cortex even wakes up. | |
| ## Usage (MLX) | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("georgeanton/alice-classifier-v2") | |
| response = generate(model, tokenizer, prompt="Classify intent: play some music", max_tokens=32) | |
| print(response) | |
| ``` | |
| ## Part of SIFTA | |
| 588 system modules | 17 biological organs | 4 provisional patents | 2,532+ commits | |
| **Repository:** [github.com/antonpictures/ANTON-SIFTA](https://github.com/antonpictures/ANTON-SIFTA) | |
| ## License | |
| Apache 2.0 β For the Swarm. πβ‘ | |