Instructions to use josu/gpt-neo-br-instruction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josu/gpt-neo-br-instruction with Transformers:

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

pipe = pipeline("text-generation", model="josu/gpt-neo-br-instruction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("josu/gpt-neo-br-instruction")
model = AutoModelForMultimodalLM.from_pretrained("josu/gpt-neo-br-instruction")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use josu/gpt-neo-br-instruction with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/josu/gpt-neo-br-instruction

SGLang

How to use josu/gpt-neo-br-instruction 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 "josu/gpt-neo-br-instruction" \
    --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": "josu/gpt-neo-br-instruction",
		"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 "josu/gpt-neo-br-instruction" \
        --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": "josu/gpt-neo-br-instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use josu/gpt-neo-br-instruction with Docker Model Runner:
```
docker model run hf.co/josu/gpt-neo-br-instruction
```



from transformers import GenerationConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("josu/gpt-neo-br-instruction")
tokenizer = AutoTokenizer.from_pretrained("josu/gpt-neo-br-instruction")


def generate_prompt(instruction, input=None):
    if input:
        return f"""Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.

### Instrução:
{instruction}

### Entrada:
{input}

### Resposta:"""
    else:
        return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.

### Instrução:
{instruction}

### Resposta:"""

generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    num_beams=4,
)
def evaluate(instruction, input=None):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    content = []
    for s in generation_output.sequences:
        output = tokenizer.decode(s)
        content.append(output.split("### Resposta:")[1].strip())
    return content

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