Instructions to use adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4 with Transformers:

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

pipe = pipeline("text-generation", model="adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4")
model = AutoModelForCausalLM.from_pretrained("adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4

SGLang

How to use adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4 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 "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4" \
    --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": "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4",
		"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 "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4" \
        --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": "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4 with Docker Model Runner:
```
docker model run hf.co/adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Como Utilizar

import transformers
import torch

model_id = "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="auto",
)

messages = [
    {"role": "system", "content": "Você é um robô pirata que sempre responde como um pirata deveria!"},
    {"role": "user", "content": "Quem é você?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|im_end|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.4

Quantizations

2 models

adalbertojunior
/

Llama-3-8B-Instruct-Portuguese-v0.4

Como Utilizar

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