--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct - phi base_model: meta-llama/Llama-2-13b datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: portuguese-tom-cat-13b results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 42.76 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 45.62 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 39.09 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 77.41 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 58.44 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 68.14 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 84.13 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 56.27 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 48.86 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-tom-cat-13b name: Open Portuguese LLM Leaderboard --- # portuguese-tom-cat-13b

This model was trained with a superset of 300,000 instructions in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Llama-2-13b # How to use ### FULL MODEL : A100 ### HALF MODEL: L4 ### 8bit or 4bit : T4 or V100 You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches. Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response. Important points like these help models to perform much better. ```python !pip install -q -U transformers !pip install -q -U accelerate !pip install -q -U bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("rhaymison/portuguese-tom-cat-13b", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/portuguese-tom-cat-13b") model.eval() ``` You can use with Pipeline. ```python from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=True, max_new_tokens=512, num_beams=2, temperature=0.3, top_k=50, top_p=0.95, early_stopping=True, pad_token_id=tokenizer.eos_token_id, ) def format_question(input:str)-> str: base_instruction = """Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.""" _input = f"""[INST] <>\n {base_instruction} <> {input} [/INST] """ return _input.strip() prompt = "Me explique sobre os romanos" pipe(format_question(prompt)) ``` ```text Os romanos foram um povo que viveu na Itália antiga, entre o século VIII a.C. e o século V d.C. Eles eram conhecidos por sua habilidade em construir estradas, edifícios e aquedutos, e também por suas conquistas militares. O Império Romano, que durou de 27 a.C. a 476 d.C., foi o maior império da história, abrangendo uma área que ia da Grécia até a Inglaterra. Os romanos também desenvolveram um sistema de leis e instituições políticas que influenciaram profundamente a cultura ocidental. ``` If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization. For the complete model in colab you will need the A100. If you want to use 4bits or 8bits, T4 or L4 will already solve the problem. # 4bits example ```python from transformers import BitsAndBytesConfig import torch nb_4bit_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) model = AutoModelForCausalLM.from_pretrained( base_model, quantization_config=bnb_config, device_map={"": 0} ) ``` # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/portuguese-tom-cat-13b) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) | Metric | Value | |--------------------------|---------| |Average |**57.86**| |ENEM Challenge (No Images)| 42.76| |BLUEX (No Images) | 45.62| |OAB Exams | 39.09| |Assin2 RTE | 77.41| |Assin2 STS | 58.44| |FaQuAD NLI | 68.14| |HateBR Binary | 84.13| |PT Hate Speech Binary | 56.27| |tweetSentBR | 48.86| ### Comments Any idea, help or report will always be welcome. email: rhaymisoncristian@gmail.com