Instructions to use Tonit23/antonio-phi2-bitagent-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tonit23/antonio-phi2-bitagent-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Tonit23/antonio-phi2-bitagent-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tonit23/antonio-phi2-bitagent-merged")
model = AutoModelForCausalLM.from_pretrained("Tonit23/antonio-phi2-bitagent-merged")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tonit23/antonio-phi2-bitagent-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tonit23/antonio-phi2-bitagent-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonit23/antonio-phi2-bitagent-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tonit23/antonio-phi2-bitagent-merged

SGLang

How to use Tonit23/antonio-phi2-bitagent-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Tonit23/antonio-phi2-bitagent-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonit23/antonio-phi2-bitagent-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Tonit23/antonio-phi2-bitagent-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonit23/antonio-phi2-bitagent-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tonit23/antonio-phi2-bitagent-merged with Docker Model Runner:
```
docker model run hf.co/Tonit23/antonio-phi2-bitagent-merged
```

🚀 Antonio Phi-2 BitAgent Merged (Subnet-20)

Autor: @Tonit23
Base: microsoft/phi-2
Fine-tune: antonio-phi2-bitagent-lora
Subnet: 🧠 Bittensor Subnet-20 — BitAgent
Publicación: octubre 2025

🧩 Descripción general

antonio-phi2-bitagent-merged es una versión LoRA-fusionada del modelo microsoft/phi-2, adaptada específicamente para el entorno BitAgent (SN20) dentro del ecosistema Bittensor Finney.

Este modelo está optimizado para tareas de razonamiento en español e inglés, inferencia compacta y tool-calling semántico (uso de funciones o herramientas internas), usando un esquema compatible con los validadores SN20.

⚙️ Detalles técnicos

Propiedad	Valor
Modelo base	`microsoft/phi-2`
Fine-tune	`LoRA` sobre dataset de prompts técnicos BFCL
Parámetros totales	~7.24 B
Parámetros entrenables	3.4 M (0.047 %)
Framework	PyTorch + Transformers + PEFT
Licencia	MIT
Hardware objetivo	CPU / GPU (float16)

Entrenamiento

El modelo fue fine-tuneado con LoRA (Low-Rank Adaptation) en un conjunto de datos mixto de tareas técnicas:

prompts de razonamiento lógico, instrucciones BFCL y tool-calling
pares entrada/salida basados en análisis ABAP y Python
mezclas en español e inglés

🧠 Uso

Inferencia local (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("Tonit23/antonio-phi2-bitagent-merged", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("Tonit23/antonio-phi2-bitagent-merged")

prompt = "Explica el proceso de staking en la red Bittensor Finney:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F16

Model tree for Tonit23/antonio-phi2-bitagent-merged

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microsoft/phi-2

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