Update README.md

### **Mecanismos para Commoditizar Tokens de LLM**
1. **Mercados de Capacidad Computacional**:
- **Modelo**: Plataformas como **Gensyn** (cripto) o **Together AI** (Web2) convierten *tiempo de GPU* en tokens transables.
- **Ejemplo**: Usuarios "alquilan" potencia de procesamiento para entrenar LLMs, recibiendo tokens intercambiables por servicios o dinero.

2. **Tokenización de Modelos de IA**:
- **NFTs de LLM**: Modelos específicos tokenizados como activos únicos (ej. **Bittensor**).
- **Licencias Comerciales**: Tokens representan derechos de uso (ej. 1 token = 1M de consultas a GPT-4).

3. **Mercados de Derivados**:
- **Futuros sobre Costo Computacional**: Contratos que apuestan al precio futuro de recursos para LLMs (ej. coste de GPU-hora en AWS).
- **Bolsa de Chicago (CME)**: Podría listar derivados vinculados a índices de coste computacional (como hace con el índice de *datacenters*).

---

### **Regulaciones Clave**
| **Jurisdicción** | **Aplicabilidad** | **Desafíos** |
|------------------|-------------------|--------------|
| **EE.UU. (SEC/CFTC)** | - Tokens como *servicios* evitan ser "valores".<br>- Si generan ganancias pasivas, pueden ser regulados como *securities*. | Diferenciar tokens de LLM de criptoactivos financieros. |
| **UE (MiCA)** | - Tokens de utilidad (*utility*) bajo MiCA Anexo II.<br>- Requieren whitepapers y auditorías. | Cumplir normas de gobernanza de IA (Ley AI Act). |
| **Singapur (MAS)** | - Enfoque en tokens de pago.<br>*Utility tokens* fuera de PSA si no son invertibles. | Evitar que la reventa los convierta en *valores*. |
| **Suiza (FINMA)** | *Tokens de activos* si respaldan derechos sobre bienes físicos (ej. GPU). | Vinculación real con infraestructura. |

---

### **Mercados Relevantes**
1. **Chicago Mercantile Exchange (CME)**:
- **Potencial**: Listar futuros sobre índices de coste computacional (ej. "Índice Hugging Face").
- **Precedente**: Futuros sobre índices de *datacenters* y ancho de banda.

2. **Bolsas de Valores Digitales**:
- **SDX (Suiza)**: Tokenización de activos físicos/digitales con custodia regulada.
- **SGX (Singapur)**: Plataforma *Marketnode* para tokenizar servicios digitales.

3. **Plataformas Descentralizadas**:
- **Bittensor (TAO)**: Mercado P2P de modelos de IA donde tokens acceden a servicios.
- **Akash Network**: Subastas de capacidad GPU usando tokens como medio de pago.

---

### **Riesgos Críticos**
- **Definición Legal Ambiguia**: ¿Son *commodities*, *servicios* o *valores*? (Ej: Caso *SEC vs. Coinbase*).
- **Volatilidad**: Costes computacionales varían por demanda, avances técnicos (ej. nuevos chips NVIDIA).
- **Sostenibilidad**: Alto consumo energético de LLMs podría generar impuestos verdes (UE).
- **Seguridad**: Tokens podrían vulnerar normas de datos (GDPR si procesan información personal).

---

### **Casos Prácticos Emergentes**
1. **Amazon AWS Tokenizado**:
- **Mecanismo**: Convertir créditos AWS en tokens ERC-20 para revender capacidad no usada.
- **Regulación**: Se rige por leyes de comercio electrónico (no MiCA).

2. **Google Cloud con Solana**:
- **Modelo**: Usar tokens SOL para pagar servicios de IA en Google Cloud (asociación 2023).

3. **Futuros sobre Coste de Entrenamiento de LLMs**:
- **Propuesta**: Derivados que cubran fluctuaciones en costes de entrenar modelos (ej. LLaMA 3).

---

### **Conclusión Estratégica**
- **Oportunidad**: Crear **índices estandarizados** (ej. "Coste por 1B de tokens procesados") para listar en bolsas.
- **Canales Prioritarios**:
- *Minoristas*: Plataformas Web3 (Bittensor, Akash).
- *Institucionales*: Derivados en CME/EUREX.
- **Regulación Clave**: Mantener tokens como *utilidad* (no rentabilidad pasiva) para evitar SEC/MiCA.

**¿Necesitas detalles sobre algún aspecto?**:
- Modelos de fijación de precios para tokens de LLM.
- Impacto de la Ley de IA de la UE.
- Ejemplos de tokenización en instituciones financieras.

README.md

@@ -1,3 +1,204 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- a-m-team/AM-DeepSeek-R1-0528-Distilled
+language:
+- en
+- es
+base_model:
+- deepseek-ai/DeepSeek-R1-0528
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]