--- language: - pt license: apache-2.0 library_name: transformers tags: - portugues - portuguese - QA - instruct - phi base_model: microsoft/Phi-3-mini-4k-instruct datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: phi-3-portuguese-tom-cat-4k-instruct 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: 61.58 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 50.63 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 43.69 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 91.54 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 75.27 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 47.46 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 83.01 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 70.19 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct 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: 57.78 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct name: Open Portuguese LLM Leaderboard --- # Phi-3-portuguese-tom-cat-4k-instruct

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 microsoft/Phi-3-mini-4k. If you are looking for enhanced compatibility, the Luana model also has a GGUF family that can be run with LlamaCpp. You can explore the GGUF models starting with the one below: - [rhaymison/phi-3-portuguese-tom-cat-4k-instruct-q8-gguf](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct-q8-gguf) - [rhaymison/phi-3-portuguese-tom-cat-4k-instruct-f16-gguf](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct-f16-gguf) Explore this and other models to find the best fit for your needs! # 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 (even smaller models like 4b) 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/phi-3-portuguese-tom-cat-4k-instruct", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct") 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_template(question:str): system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido." return f"""<|system|> { system_prompt } <|user|> { question } <|assistant|> """ question = format_template("E possivel ir de Carro dos Estados unidos ate o japão") pipe(question) ``` 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/phi-3-portuguese-tom-cat-4k-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) | Metric | Value | |--------------------------|---------| |Average |**64.57**| |ENEM Challenge (No Images)| 61.58| |BLUEX (No Images) | 50.63| |OAB Exams | 43.69| |Assin2 RTE | 91.54| |Assin2 STS | 75.27| |FaQuAD NLI | 47.46| |HateBR Binary | 83.01| |PT Hate Speech Binary | 70.19| |tweetSentBR | 57.78| ### Comments Any idea, help or report will always be welcome. email: rhaymisoncristian@gmail.com