matheusrdgsf/cesar-ptbr

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: gptq
• bits: 8
• tokenizer: None
• dataset: None
• group_size: 32
• damp_percent: 0.1
• desc_act: True
• sym: True
• true_sequential: True
• use_cuda_fp16: False
• model_seqlen: 4096
• block_name_to_quantize: model.layers
• module_name_preceding_first_block: ['model.embed_tokens']
• batch_size: 1
• pad_token_id: None
• disable_exllama: True
• max_input_length: None

Framework versions

Load model AutoModel

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

config = PeftConfig.from_pretrained("matheusrdgsf/cesar-ptbr")
model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GPTQ", revision="gptq-8bit-32g-actorder_True", device_map='auto')
model = PeftModel.from_pretrained(model, "matheusrdgsf/cesar-ptbr")

Easy inference

from transformers import GenerationConfig
from transformers import AutoTokenizer

tokenizer_model = AutoTokenizer.from_pretrained('TheBloke/zephyr-7B-beta-GPTQ')
tokenizer_template = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-alpha')

generation_config = GenerationConfig(
    do_sample=True,
    temperature=0.1,
    top_p=0.25,
    top_k=0,
    max_new_tokens=512,
    repetition_penalty=1.1,
    eos_token_id=tokenizer_model.eos_token_id,
    pad_token_id=tokenizer_model.eos_token_id,
)


def get_inference(
    text,
    model,
    tokenizer_model=tokenizer_model,
    tokenizer_template=tokenizer_template,
    generation_config=generation_config,
):
    st_time = time.time()
    inputs = tokenizer_model(
        tokenizer_template.apply_chat_template(
            [
                {
                    "role": "system",
                    "content": "Você é um chatbot para indicação de filmes. Responda em português e de maneira educada sugestões de filmes para os usuários.",
                },
                {"role": "user", "content": text},
            ],
            tokenize=False,
        ),
        return_tensors="pt",
    ).to("cuda")

    outputs = model.generate(**inputs, generation_config=generation_config)

    print('inference time:', time.time() - st_time)
    return tokenizer_model.decode(outputs[0], skip_special_tokens=True).split('\n')[-1]

get_inference('Poderia indicar filmes de ação de até 2 horas?', model)

• PEFT 0.5.0

Open Portuguese LLM Leaderboard Evaluation Results

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

Metric	Value
Average	59.22
ENEM Challenge (No Images)	53.74
BLUEX (No Images)	46.87
OAB Exams	38.27
Assin2 RTE	58.32
Assin2 STS	68.49
FaQuAD NLI	73.81
HateBR Binary	83.30
PT Hate Speech Binary	67.49
tweetSentBR	42.71