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

Libraries

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

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

pipe = pipeline("text-generation", model="adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3")
model = AutoModelForCausalLM.from_pretrained("adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3 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-Dolphin-Portuguese-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3 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-Dolphin-Portuguese-v0.3" \
    --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": "adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3",
		"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 "adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3" \
        --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": "adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Model Card for Model ID

Model Card for Llama-3-8B-Dolphin-Portuguese-v0.3

Model Trained on a translated version of dolphin dataset.

Usage

import transformers
import torch

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

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="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("<|eot_id|>")
]

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):])

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the 🚀 Open Portuguese LLM Leaderboard

Metric	Value
Average	73.15
ENEM Challenge (No Images)	68.86
BLUEX (No Images)	57.86
OAB Exams	61.91
Assin2 RTE	93.05
Assin2 STS	76.48
FaQuAD NLI	76.78
HateBR Binary	83.25
PT Hate Speech Binary	68.85
tweetSentBR	71.30

Downloads last month: 18

Safetensors

Model size

8B params

Tensor type

BF16

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

Merges

1 model

Quantizations

3 models

Spaces using adalbertojunior/Llama-3-8B-Dolphin-Portuguese-v0.3 10

Evaluation results

accuracy on ENEM Challenge (No Images)
Open Portuguese LLM Leaderboard

68.860
accuracy on BLUEX (No Images)
Open Portuguese LLM Leaderboard

57.860
accuracy on OAB Exams
Open Portuguese LLM Leaderboard

61.910
f1-macro on Assin2 RTE
test set Open Portuguese LLM Leaderboard

93.050
pearson on Assin2 STS
test set Open Portuguese LLM Leaderboard

76.480
f1-macro on FaQuAD NLI
test set Open Portuguese LLM Leaderboard

76.780
f1-macro on HateBR Binary
test set Open Portuguese LLM Leaderboard

83.250
f1-macro on PT Hate Speech Binary
test set Open Portuguese LLM Leaderboard

68.850