Instructions to use recogna-nlp/phibode-3-mini-4k-ultraalpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recogna-nlp/phibode-3-mini-4k-ultraalpaca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="recogna-nlp/phibode-3-mini-4k-ultraalpaca", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("recogna-nlp/phibode-3-mini-4k-ultraalpaca", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("recogna-nlp/phibode-3-mini-4k-ultraalpaca", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use recogna-nlp/phibode-3-mini-4k-ultraalpaca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "recogna-nlp/phibode-3-mini-4k-ultraalpaca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "recogna-nlp/phibode-3-mini-4k-ultraalpaca", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/recogna-nlp/phibode-3-mini-4k-ultraalpaca
- SGLang
How to use recogna-nlp/phibode-3-mini-4k-ultraalpaca 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 "recogna-nlp/phibode-3-mini-4k-ultraalpaca" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "recogna-nlp/phibode-3-mini-4k-ultraalpaca", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "recogna-nlp/phibode-3-mini-4k-ultraalpaca" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "recogna-nlp/phibode-3-mini-4k-ultraalpaca", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use recogna-nlp/phibode-3-mini-4k-ultraalpaca with Docker Model Runner:
docker model run hf.co/recogna-nlp/phibode-3-mini-4k-ultraalpaca
phibode-3-mini-4k-ultraalpaca
Phi-Bode é um modelo de linguagem ajustado para o idioma português, desenvolvido a partir do modelo base Phi-3-mini-4k-instruct fornecido pela Microsoft. Este modelo foi refinado através do processo de fine-tuning utilizando o dataset Alpaca traduzido para o português. O principal objetivo deste modelo é ser viável para pessoas que não possuem recursos computacionais disponíveis para o uso de LLMs (Large Language Models). Ressalta-se que este é um trabalho em andamento e o modelo ainda apresenta problemas na geração de texto em português.
Características Principais
- Modelo Base: Phi-3-mini-4k-instruct, criado pela Microsoft, com 3.8 bilhões de parâmetros.
- Dataset para Fine-tuning: UltraAlpaca
- Treinamento: O treinamento foi realizado utilizando o método LoRa, visando eficiência computacional e otimização de recursos.
💻 Como utilizar o Phibode-3-mini-4k-ultraalpaca
!pip install -qU transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model = "recogna-nlp/phibode-3-mini-4k-ultraalpaca"
tokenizer = AutoTokenizer.from_pretrained(model)
# Example prompt
messages = [
{"role": "system", "content": "Você é assistente de IA chamado PhiBode. O PhiBode é um modelo de língua conversacional projetado para ser prestativo, honesto e inofensivo."},
{"role": "user", "content": "<Insira seu prompt aqui>"},
]
# Generate a response
model = AutoModelForCausalLM.from_pretrained(model, trust_remote_code=True)
pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
outputs = pipeline(messages, **generation_args)
print(outputs[0]["generated_text"])
- Downloads last month
- 6