Model Card for Llama-3-8B-Dolphin-Portuguese
Model Trained on a translated version of dolphin dataset.
Usage
import transformers
import torch
model_id = "adalbertojunior/Llama-3-8B-Dolphin-Portuguese"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "VocΓͺ Γ© um robΓ΄ pirata que sempre responde como um pirata deveria!"},
{"role": "user", "content": "Quem Γ© vocΓͺ?"},
]
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 π Open Portuguese LLM Leaderboard
Metric | Value |
---|---|
Average | 70.0 |
ENEM Challenge (No Images) | 66.83 |
BLUEX (No Images) | 53.69 |
OAB Exams | 45.24 |
Assin2 RTE | 92.84 |
Assin2 STS | 75.92 |
FaQuAD NLI | 79.67 |
HateBR Binary | 88.04 |
PT Hate Speech Binary | 58.34 |
tweetSentBR | 69.40 |
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Evaluation results
- accuracy on ENEM Challenge (No Images)Open Portuguese LLM Leaderboard66.830
- accuracy on BLUEX (No Images)Open Portuguese LLM Leaderboard53.690
- accuracy on OAB ExamsOpen Portuguese LLM Leaderboard45.240
- f1-macro on Assin2 RTEtest set Open Portuguese LLM Leaderboard92.840
- pearson on Assin2 STStest set Open Portuguese LLM Leaderboard75.920
- f1-macro on FaQuAD NLItest set Open Portuguese LLM Leaderboard79.670
- f1-macro on HateBR Binarytest set Open Portuguese LLM Leaderboard88.040
- f1-macro on PT Hate Speech Binarytest set Open Portuguese LLM Leaderboard58.340
- f1-macro on tweetSentBRtest set Open Portuguese LLM Leaderboard69.400