| | --- |
| | language: ["es", "en"] |
| | license: apache-2.0 |
| | tags: |
| | - bittensor |
| | - subnet-20 |
| | - bitagent |
| | - finney |
| | - tao |
| | - tool-calling |
| | - bfcl |
| | - reasoning |
| | - agent |
| | base_model: Salesforce/xLAM-7b-r |
| | pipeline_tag: text-generation |
| | model-index: |
| | - name: antonio-bfcl-toolmodel |
| | results: |
| | - task: |
| | type: text-generation |
| | name: Generative reasoning and tool-calling |
| | metrics: |
| | - type: accuracy |
| | value: 0.0 |
| | --- |
| | |
| | # 馃 Antonio BFCL Toolmodel |
| |
|
| | Este modelo forma parte del ecosistema **BitAgent (Subnet-20)** de Bittensor, dise帽ado para tareas de *tool-calling*, razonamiento l贸gico estructurado y generaci贸n de texto contextual. |
| | Optimizado para comunicaci贸n eficiente entre agentes dentro del protocolo Finney. |
| |
|
| | --- |
| |
|
| | ## 馃殌 Descripci贸n t茅cnica |
| |
|
| | **antonio-bfcl-toolmodel** est谩 basado en un modelo open-source tipo `xLAM-7b-r`, ajustado para: |
| |
|
| | - 馃搳 *Razonamiento simb贸lico y factual multiling眉e* |
| | - 馃З *Tool-calling autom谩tico* (formato JSON conforme a los prompts de Subnet-20) |
| | - 馃攧 *Respuestas deterministas* con `temperature=0.1` y `top_p=0.9` |
| | - 鈿欙笍 *Compatibilidad total con el pipeline de BitAgent Miner (v1.0.51)* |
| | - 馃寪 *Idiomas soportados*: Espa帽ol 馃嚜馃嚫 e Ingl茅s 馃嚞馃嚙 |
| |
|
| | --- |
| |
|
| | ## 馃З Integraci贸n con Subnet-20 |
| |
|
| | Los validadores pueden invocar este modelo a trav茅s de los protocolos: |
| |
|
| | - `QueryTask` |
| | - `QueryResult` |
| | - `IsAlive` |
| | - `GetHFModelName` |
| | - `SetHFModelName` |
| |
|
| | El modelo responde mediante `bittensor.dendrite` y cumple con la especificaci贸n **BitAgent v1.0.51**. |
| |
|
| | --- |
| |
|
| | ## 馃 Ejemplo de inferencia local |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | |
| | model_name = "Tonit23/antonio-bfcl-toolmodel" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForCausalLM.from_pretrained(model_name) |
| | |
| | prompt = "Resuelve esta operaci贸n: 12 + 37 = " |
| | inputs = tokenizer(prompt, return_tensors="pt") |
| | outputs = model.generate(**inputs, max_new_tokens=32) |
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| | |
