| --- |
| language: es |
| license: apache-2.0 |
| tags: |
| - text-generation |
| - transformer |
| - pytorch |
| --- |
| |
| # MTP Mini - Modelo Mejorado 20x |
|
|
| Modelo transformer con arquitectura avanzada entrenado en GPU T4. |
|
|
| ## Arquitectura |
| - **Parámetros**: ~310.7M (310,708,225) |
| - **Vocabulario**: 8000 tokens |
| - **Capas**: 24 |
| - **Dimensión**: 1024 |
| - **Contexto**: 2048 tokens |
|
|
| ## Mejoras |
| - ✅ RoPE, RMSNorm, SwiGLU |
| - ✅ Flash Attention |
| - ✅ Gradient Checkpointing |
| - ✅ Mixed Precision FP16 |
| - ✅ Anti-alucinación |
| - ✅ Confidence Scoring |
|
|
| ## Uso |
| ```python |
| import torch, pickle |
| from tokenizer import MTPTokenizer |
| from model import MTPMiniModel |
| |
| with open('mtp_mini.pkl', 'rb') as f: |
| data = pickle.load(f) |
| |
| tokenizer = MTPTokenizer('mtp_tokenizer.model') |
| model = MTPMiniModel(**data['config']['model']) |
| model.load_state_dict(data['model_state_dict']) |
| model.eval() |
| |
| prompt = "¿Qué es la IA?" |
| ids = torch.tensor([tokenizer.encode(prompt)]).unsqueeze(0) |
| output = model.generate(ids, max_new_tokens=150) |
| print(tokenizer.decode(output[0].tolist())) |
| ``` |
|
|
| Entrenado en Google Colab con GPU T4. |
|
|